Method for providing XR content and XR device for providing XR content

ABSTRACT

A method for providing XR content includes obtaining a first 2D image representing a user&#39;s face, determining whether an object which is not the user&#39;s face is included in the first 2D image, in response to determining that the object is included in the acquired first 2D image, generating a second 2D image representing the user&#39;s face without the object based on the first 2D image, generating a first 3D image corresponding to the generated second 2D image, generating a mask image representing a difference between the first 2D image and the second 2D image, determining a type of object based on the generated mask image, obtaining a 3D preset including one or more 3D images corresponding to the determined type of object, generating a second 3D by combining at least one of one or more 3D images with the first 3D image and providing XR content including the generated second 3D image.

This application claims the priority benefit of Korean PatentApplication No. 10-2019-0104925, filed on Aug. 27, 2019 in the Republicof Korea, which is hereby incorporated by reference as if fully setforth herein.

TECHNICAL FIELD

This relates generally to an extended reality (XR) device for providingaugmented reality (AR) mode and virtual reality (VR) mode and a methodof controlling the same. More particularly, the present disclosure isapplicable to all of the technical fields of 5^(th) generation (5G)communication, robots, self-driving, and artificial intelligence (AI).

BACKGROUND

VR (Virtual Reality) technology creates a simulated environment byproviding CG (Computer Graphic) image/video data that can be similar toor different from the real world. AR (Augmented Reality) technologyprovides CG image/video data generated by overlaying content on the realworld. MR (Mixed Reality) technology (referred to as hybrid reality) isthe merging of real and virtual worlds to provide new environments wherephysical and virtual objects co-exists and interact in real time. XR(Extended reality) technology refers to all real and virtualenvironments and can cover all the various forms of computer-alteredreality, including: VR, AR, and MR. The development of XR devicesproviding XR contents has increased significantly in recent years. TheXR device for providing XR content (hereinafter referred to as “XRdevice”) according to embodiments of the present disclosure may createXR content that includes images (e.g., emoji, avatar, etc.) createdbased on a 2D user image (e.g., a facial image of the user) acquired bya camera sensor or the like, and may provide the created XR content.However, in the case of user wearing glasses, when the XR device forproviding XR content generates 3D image/video based on user's 2Dimage/video acquired, the device may not provide accurate user's 3Dimage/video, if the glasses are recognized as part of the face.

SUMMARY

Accordingly, there is a need for XR devices with improved methods forproviding XR content for various service. Therefore, when user's 2Dimage are acquired, XR device for providing XR content determineswhether the other objects (e.g., glasses, sunglasses etc.) except theface are included, generates 3D image based on 2D image that the otherobjects are removed, generates 3D image related to the objects byanalyzing image of the things, and then generates the final 3D imagederived from two combined 3D images. Such methods and XR devicesprovides user's 3D image with higher accuracy Such methods and XRdevices device can provides a higher user experience.

The above deficiencies and other problems associated with the XR devicefor providing the XR content are reduced or eliminated by the disclosedXR device and methods. In accordance with some embodiments, a method forproviding XR content includes obtaining a first 2D image representing auser's face, determining whether an object which is not the user's faceis included in the first 2D image, in response to determining that theobject is included in the acquired first 2D image, generating a second2D image representing the user's face without the object based on thefirst 2D image, generating a first 3D image corresponding to thegenerated second 2D image, generating a mask image representing adifference between the first 2D image and the second 2D image,determining a type of object based on the generated mask image,obtaining a 3D preset including one or more 3D images corresponding tothe determined type of object, generating a second 3D by combining atleast one of one or more 3D images with the first 3D image and providingXR content including the generated second 3D image.

In accordance with some embodiments, an XR device for providing XRcontent includes an image/video analyzer configured to obtain a first 2Dimage representing a user's face, determine whether an object which isnot the user's face is included in the first 2D image. In someembodiments, in response to determining that the object is included inthe acquired first 2D image, the image/video analyzer is furtherconfigured to generate a second 2D image representing the user's facewithout the object based on the first 2D image, generate a mask imagerepresenting a difference between the first 2D image and the second 2Dimage and determine a type of object based on the generated mask image.In some embodiments, the XR device further includes a 3D image/videoprocessor configured to generate a first 3D image corresponding to thegenerated second 2D image, obtain a 3D preset including one or more 3Dimages corresponding to the determined type of object, and generate asecond 3D by combining at least one of one or more 3D images with thefirst 3D image. In some embodiments, the XR device further includes adisplay configured to providing XR content including the generatedsecond 3D image.

In accordance with some embodiments, an XR device for providing XRcontent includes one or more processors, a display, and a memory storingone or more programs. In some embodiments, the one or more programs areconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: determining whether an object whichis not a user's face is included in a first 2D image representing theuser's face, in response to determining that the object is included inthe acquired first 2D image, generating a second 2D image representingthe user's face without the object based on the first 2D image,generating a first 3D image corresponding to the generated second 2Dimage, generating a mask image representing a difference between thefirst 2D image and the second 2D image, determining a type of objectbased on the generated mask image, obtaining a 3D preset including oneor more 3D images corresponding to the determined type of object andgenerating a second 3D by combining at least one of one or more 3Dimages with the first 3D image. In some embodiments, the display isfurther configured to provide XR content including the generated second3D image.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the various described embodiments,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1 is a diagram illustrating an exemplary resource grid to whichphysical signals/channels are mapped in a 3^(rd) generation partnershipproject (3GPP) system;

FIG. 2 is a diagram illustrating an exemplary method of transmitting andreceiving 3GPP signals;

FIG. 3 is a diagram illustrating an exemplary structure of asynchronization signal block (SSB);

FIG. 4 is a diagram illustrating an exemplary random access procedure;

FIG. 5 is a diagram illustrating exemplary uplink (UL) transmissionbased on a UL grant;

FIG. 6 is a conceptual diagram illustrating exemplary physical channelprocessing;

FIG. 7 is a block diagram illustrating an exemplary transmitter andreceiver for hybrid beamforming;

FIG. 8(a) is a diagram illustrating an exemplary narrowband operation,and FIG. 8(b) is a diagram illustrating exemplary machine typecommunication (MTC) channel repetition with radio frequency (RF)retuning;

FIG. 9 is a block diagram illustrating an exemplary wirelesscommunication system to which proposed methods according to the presentdisclosure are applicable;

FIG. 10 is a block diagram illustrating an artificial intelligence (AI)device 100 according to an embodiment of the present disclosure;

FIG. 11 is a block diagram illustrating an AI server 200 according to anembodiment of the present disclosure;

FIG. 12 is a diagram illustrating an AI system 1 according to anembodiment of the present disclosure;

FIG. 13 is a block diagram illustrating an extended reality (XR) deviceaccording to embodiments of the present disclosure;

FIG. 14 is a detailed block diagram illustrating a memory illustrated inFIG. 13;

FIG. 15 is a block diagram illustrating a point cloud data processingsystem;

FIG. 16 is a block diagram illustrating a device including a learningprocessor;

FIG. 17 is a flowchart illustrating a process of providing an XR serviceby an XR device 1600 of the present disclosure, illustrated in FIG. 16;

FIG. 18 is a diagram illustrating the outer appearances of an XR deviceand a robot;

FIG. 19 is a flowchart illustrating a process of controlling a robot byusing an XR device;

FIG. 20 is a diagram illustrating a vehicle that provides a self-drivingservice;

FIG. 21 is a flowchart illustrating a process of providing an augmentedreality/virtual reality (AR/VR) service during a self-driving service inprogress;

FIG. 22 is a conceptual diagram illustrating an exemplary method forimplementing an XR device using an HMD type according to an embodimentof the present disclosure.

FIG. 23 is a conceptual diagram illustrating an exemplary method forimplementing an XR device using AR glasses according to an embodiment ofthe present disclosure.

FIG. 24 represents 3D image of a user in accordance with someembodiments.

FIG. 25 represents a block diagram of XR device for providing XR contentin accordance with some embodiments.

FIG. 26 represents a method for providing XR content in accordance withsome embodiments.

FIG. 27 represents a training process of deep learning operations of afirst network and a second network in accordance with some embodiments.

FIG. 28 represents XR content in accordance with some embodiments.

FIG. 29 is a flow diagram of a method for providing XR content inaccordance with some embodiments.

FIG. 30 is a conceptual diagram illustrating an exemplary case in whichthe XR device is applied to a clothing-related device according to anembodiment of the present disclosure.

DESCRIPTION OF SPECIFIC EMBODIMENTS

Reference will now be made in detail to embodiments of the presentdisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts, and aredundant description will be avoided. The terms “module” and “unit” areinterchangeably used only for easiness of description and thus theyshould not be considered as having distinctive meanings or roles.Further, a detailed description of well-known technology will not begiven in describing embodiments of the present disclosure lest it shouldobscure the subject matter of the embodiments. The attached drawings areprovided to help the understanding of the embodiments of the presentdisclosure, not limiting the scope of the present disclosure. It is tobe understood that the present disclosure covers various modifications,equivalents, and/or alternatives falling within the scope and spirit ofthe present disclosure.

The following embodiments of the present disclosure are intended toembody the present disclosure, not limiting the scope of the presentdisclosure. What could easily be derived from the detailed descriptionof the present disclosure and the embodiments by a person skilled in theart is interpreted as falling within the scope of the presentdisclosure.

The above embodiments are therefore to be construed in all aspects asillustrative and not restrictive. The scope of the disclosure should bedetermined by the appended claims and their legal equivalents, not bythe above description, and all changes coming within the meaning andequivalency range of the appended claims are intended to be embracedtherein.

INTRODUCTION

In the disclosure, downlink (DL) refers to communication from a basestation (BS) to a user equipment (UE), and uplink (UL) refers tocommunication from the UE to the BS. On DL, a transmitter may be a partof the BS and a receiver may be a part of the UE, whereas on UL, atransmitter may be a part of the UE and a receiver may be a part of theBS. A UE may be referred to as a first communication device, and a BSmay be referred to as a second communication device in the presentdisclosure. The term BS may be replaced with fixed station, Node B,evolved Node B (eNB), next generation Node B (gNB), base transceiversystem (BTS), access point (AP), network or 5^(th) generation (5G)network node, artificial intelligence (AI) system, road side unit (RSU),robot, augmented reality/virtual reality (AR/VR) system, and so on. Theterm UE may be replaced with terminal, mobile station (MS), userterminal (UT), mobile subscriber station (MSS), subscriber station (SS),advanced mobile station (AMS), wireless terminal (WT), device-to-device(D2D) device, vehicle, robot, AI device (or module), AR/VR device (ormodule), and so on.

The following technology may be used in various wireless access systemsincluding code division multiple access (CDMA), frequency divisionmultiple access (FDMA), time division multiple access (TDMA), orthogonalfrequency division multiple access (OFDMA), and single carrier FDMA(SC-FDMA).

For the convenience of description, the present disclosure is describedin the context of a 3^(rd) generation partnership project (3GPP)communication system (e.g., long term evolution-advanced (LTE-A) and newradio or new radio access technology (NR)), which should not beconstrued as limiting the present disclosure. For reference, 3GPP LTE ispart of evolved universal mobile telecommunications system (E-UMTS)using evolved UMTS terrestrial radio access (E-UTRA), and LTE-A/LTE-Apro is an evolution of 3GPP LTE. 3GPP NR is an evolution of3GPP/LTE-A/LTE-A pro.

In the present disclosure, a node refers to a fixed point capable oftransmitting/receiving wireless signals by communicating with a UE.Various types of BSs may be used as nodes irrespective of their names.For example, any of a BS, an NB, an eNB, a pico-cell eNB (PeNB), a homeeNB (HeNB), a relay, and a repeater may be a node. At least one antennais installed in one node. The antenna may refer to a physical antenna,an antenna port, a virtual antenna, or an antenna group. A node is alsoreferred to as a point.

In the present disclosure, a cell may refer to a certain geographicalarea or radio resources, in which one or more nodes provide acommunication service. A “cell” as a geographical area may be understoodas coverage in which a service may be provided in a carrier, while a“cell” as radio resources is associated with the size of a frequencyconfigured in the carrier, that is, a bandwidth (BW). Because a range inwhich a node may transmit a valid signal, that is, DL coverage and arange in which the node may receive a valid signal from a UE, that is,UL coverage depend on a carrier carrying the signals, and thus thecoverage of the node is associated with the “cell” coverage of radioresources used by the node. Accordingly, the term “cell” may mean theservice overage of a node, radio resources, or a range in which a signalreaches with a valid strength in the radio resources, undercircumstances.

In the present disclosure, communication with a specific cell may amountto communication with a BS or node that provides a communication serviceto the specific cell. Further, a DL/UL signal of a specific cell means aDL/UL signal from/to a BS or node that provides a communication serviceto the specific cell. Particularly, a cell that provides a UL/DLcommunication service to a UE is called a serving cell for the UE.Further, the channel state/quality of a specific cell refers to thechannel state/quality of a channel or a communication link establishedbetween a UE and a BS or node that provides a communication service tothe specific cell.

A “cell” associated with radio resources may be defined as a combinationof DL resources and UL resources, that is, a combination of a DLcomponent carrier (CC) and a UL CC. A cell may be configured with DLresources alone or both DL resources and UL resources in combination.When carrier aggregation (CA) is supported, linkage between the carrierfrequency of DL resources (or a DL CC) and the carrier frequency of ULresources (or a UL CC) may be indicated by system informationtransmitted in a corresponding cell. A carrier frequency may beidentical to or different from the center frequency of each cell or CC.Hereinbelow, a cell operating in a primary frequency is referred to as aprimary cell (Pcell) or PCC, and a cell operating in a secondaryfrequency is referred to as a secondary cell (Scell) or SCC. The Scellmay be configured after a UE and a BS perform a radio resource control(RRC) connection establishment procedure and thus an RRC connection isestablished between the UE and the BS, that is, the UE is RRC_CONNECTED.The RRC connection may mean a path in which the RRC of the UE mayexchange RRC messages with the RRC of the BS. The Scell may beconfigured to provide additional radio resources to the UE. The Scelland the Pcell may form a set of serving cells for the UE according tothe capabilities of the UE. Only one serving cell configured with aPcell exists for an RRC_CONNECTED UE which is not configured with CA ordoes not support CA.

A cell supports a unique radio access technology (RAT). For example, LTERAT-based transmission/reception is performed in an LTE cell, and 5GRAT-based transmission/reception is performed in a 5G cell.

CA aggregates a plurality of carriers each having a smaller system BWthan a target BW to support broadband. CA differs from OFDMA in that DLor UL communication is conducted in a plurality of carrier frequencieseach forming a system BW (or channel BW) in the former, and DL or ULcommunication is conducted by loading a basic frequency band dividedinto a plurality of orthogonal subcarriers in one carrier frequency inthe latter. In OFDMA or orthogonal frequency division multiplexing(OFDM), for example, one frequency band having a certain system BW isdivided into a plurality of subcarriers with a predetermined subcarrierspacing, information/data is mapped to the plurality of subcarriers, andthe frequency band in which the information/data has been mapped istransmitted in a carrier frequency of the frequency band throughfrequency upconversion. In wireless CA, frequency bands each having asystem BW and a carrier frequency may be used simultaneously forcommunication, and each frequency band used in CA may be divided into aplurality of subcarriers with a predetermined subcarrier spacing.

The 3GPP communication standards define DL physical channelscorresponding to resource elements (REs) conveying informationoriginated from upper layers of the physical layer (e.g., the mediumaccess control (MAC) layer, the radio link control (RLC) layer, thepacket data convergence protocol (PDCP) layer, the radio resourcecontrol (RRC) layer, the service data adaptation protocol (SDAP) layer,and the non-access stratum (NAS) layer), and DL physical signalscorresponding to REs which are used in the physical layer but do notdeliver information originated from the upper layers. For example,physical downlink shared channel (PDSCH), physical broadcast channel(PBCH), physical multicast channel (PMCH), physical control formatindicator channel (PCFICH), and physical downlink control channel(PDCCH) are defined as DL physical channels, and a reference signal (RS)and a synchronization signal are defined as DL physical signals. An RS,also called a pilot is a signal in a predefined special waveform knownto both a BS and a UE. For example, cell specific RS (CRS), UE-specificRS (UE-RS), positioning RS (PRS), channel state information RS (CSI-RS),and demodulation RS (DMRS) are defined as DL RSs. The 3GPP communicationstandards also define UL physical channels corresponding to REsconveying information originated from upper layers, and UL physicalsignals corresponding to REs which are used in the physical layer but donot carry information originated from the upper layers. For example,physical uplink shared channel (PUSCH), physical uplink control channel(PUCCH), and physical random access channel (PRACH) are defined as ULphysical channels, and DMRS for a UL control/data signal and soundingreference signal (SRS) used for UL channel measurement are defined.

In the present disclosure, physical shared channels (e.g., PUSCH andPDSCH) are used to deliver information originated from the upper layersof the physical layer (e.g., the MAC layer, the RLC layer, the PDCPlayer, the RRC layer, the SDAP layer, and the NAS layer).

In the present disclosure, an RS is a signal in a predefined specialwaveform known to both a BS and a UE. In a 3GPP communication system,for example, the CRS being a cell common RS, the UE-RS for demodulationof a physical channel of a specific UE, the CSI-RS used tomeasure/estimate a DL channel state, and the DMRS used to demodulate aphysical channel are defined as DL RSs, and the DMRS used fordemodulation of a UL control/data signal and the SRS used for UL channelstate measurement/estimation are defined as UL RSs.

In the present disclosure, a transport block (TB) is payload for thephysical layer. For example, data provided to the physical layer by anupper layer or the MAC layer is basically referred to as a TB. A UEwhich is a device including an AR/VR module (i.e., an AR/VR device) maytransmit a TB including AR/VR data to a wireless communication network(e.g., a 5G network) on a PUSCH. Further, the UE may receive a TBincluding AR/VR data of the 5G network or a TB including a response toAR/VR data transmitted by the UE from the wireless communicationnetwork.

In the present disclosure, hybrid automatic repeat and request (HARQ) isa kind of error control technique. An HARQ acknowledgement (HARQ-ACK)transmitted on DL is used for error control of UL data, and a HARQ-ACKtransmitted on UL is used for error control of DL data. A transmitterperforming an HARQ operation awaits reception of an ACK aftertransmitting data (e.g., a TB or a codeword). A receiver performing anHARQ operation transmits an ACK only when data has been successfullyreceived, and a negative ACK (NACK) when the received data has an error.Upon receipt of the ACK, the transmitter may transmit (new) data, andupon receipt of the NACK, the transmitter may retransmit the data.

In the present disclosure, CSI generically refers to informationrepresenting the quality of a radio channel (or link) establishedbetween a UE and an antenna port. The CSI may include at least one of achannel quality indicator (CQI), a precoding matrix indicator (PMI), aCSI-RS resource indicator (CRI), a synchronization signal block resourceindicator (SSBRI), a layer indicator (LI), a rank indicator (RI), or areference signal received power (RSRP).

In the present disclosure, frequency division multiplexing (FDM) istransmission/reception of signals/channels/users in different frequencyresources, and time division multiplexing (TDM) istransmission/reception of signals/channels/users in different timeresources.

In the present disclosure, frequency division duplex (FDD) is acommunication scheme in which UL communication is performed in a ULcarrier, and DL communication is performed in a DL carrier linked to theUL carrier, whereas time division duplex (TDD) is a communication schemein which UL communication and DL communication are performed in timedivision in the same carrier. In the present disclosure, half-duplex isa scheme in which a communication device operates on UL or UL only inone frequency at one time point, and on DL or UL in another frequency atanother time point. For example, when the communication device operatesin half-duplex, the communication device communicates in UL and DLfrequencies, wherein the communication device performs a UL transmissionin the UL frequency for a predetermined time, and retunes to the DLfrequency and performs a DL reception in the DL frequency for anotherpredetermined time, in time division, without simultaneously using theUL and DL frequencies.

FIG. 1 is a diagram illustrating an exemplary resource grid to whichphysical signals/channels are mapped in a 3GPP system.

Referring to FIG. 1, for each subcarrier spacing configuration andcarrier, a resource grid of N^(size,μ) _(grid)*N^(RB) _(sc) subcarriersby 14*2μ OFDM symbols is defined. Herein, N^(size,μ) _(grid) isindicated by RRC signaling from a BS, and μ represents a subcarrierspacing Δf given by Δf=2μ*15 [kHz] where μ∈ {0, 1, 2, 3, 4} in a 5Gsystem.

N may be different between UL and DL as well as a subcarrier spacingconfiguration. For the subcarrier spacing configuration g, an antennaport p, and a transmission direction (UL or DL), there is one resourcegrid. Each element of a resource grid for the subcarrier spacingconfiguration g and the antenna port p is referred to as an RE, uniquelyidentified by an index pair (k,l) where k is a frequency-domain indexand l is the position of a symbol in a relative time domain with respectto a reference point. A frequency unit used for mapping physicalchannels to REs, resource block (RB) is defined by 12 consecutivesubcarriers (N^(RB) _(sc)=12) in the frequency domain. Considering thata UE may not support a wide BW supported by the 5G system at one time,the UE may be configured to operate in a part (referred to as abandwidth part (BWP)) of the frequency BW of a cell.

For the background technology, terminology, and abbreviations used inthe present disclosure, standard specifications published before thepresent disclosure may be referred to. For example, the followingdocuments may be referred to.

3GPP LTE

-   -   3GPP TS 36.211: Physical channels and modulation    -   3GPP TS 36.212: Multiplexing and channel coding    -   3GPP TS 36.213: Physical layer procedures    -   3GPP TS 36.214: Physical layer; Measurements    -   3GPP TS 36.300: Overall description    -   3GPP TS 36.304: User Equipment (UE) procedures in idle mode    -   3GPP TS 36.314: Layer 2-Measurements    -   3GPP TS 36.321: Medium Access Control (MAC) protocol    -   3GPP TS 36.322: Radio Link Control (RLC) protocol    -   3GPP TS 36.323: Packet Data Convergence Protocol (PDCP)    -   3GPP TS 36.331: Radio Resource Control (RRC) protocol    -   3GPP TS 23.303: Proximity-based services (Prose); Stage 2    -   3GPP TS 23.285: Architecture enhancements for V2X services    -   3GPP TS 23.401: General Packet Radio Service (GPRS) enhancements        for Evolved Universal Terrestrial Radio Access Network (E-UTRAN)        access    -   3GPP TS 23.402: Architecture enhancements for non-3GPP accesses    -   3GPP TS 23.286: Application layer support for V2X services;        Functional architecture and information flows    -   3GPP TS 24.301: Non-Access-Stratum (NAS) protocol for Evolved        Packet System (EPS); Stage 3    -   3GPP TS 24.302: Access to the 3GPP Evolved Packet Core (EPC) via        non-3GPP access networks; Stage 3    -   3GPP TS 24.334: Proximity-services (ProSe) User Equipment (UE)        to ProSe function protocol aspects; Stage 3    -   3GPP TS 24.386: User Equipment (UE) to V2X control function;        protocol aspects; Stage 3

3GPP NR (e.g. 5G)

-   -   3GPP TS 38.211: Physical channels and modulation    -   3GPP TS 38.212: Multiplexing and channel coding    -   3GPP TS 38.213: Physical layer procedures for control    -   3GPP TS 38.214: Physical layer procedures for data    -   3GPP TS 38.215: Physical layer measurements    -   3GPP TS 38.300: NR and NG-RAN Overall Description    -   3GPP TS 38.304: User Equipment (UE) procedures in idle mode and        in RRC inactive state    -   3GPP TS 38.321: Medium Access Control (MAC) protocol    -   3GPP TS 38.322: Radio Link Control (RLC) protocol    -   3GPP TS 38.323: Packet Data Convergence Protocol (PDCP)    -   3GPP TS 38.331: Radio Resource Control (RRC) protocol    -   3GPP TS 37.324: Service Data Adaptation Protocol (SDAP)    -   3GPP TS 37.340: Multi-connectivity; Overall description    -   3GPP TS 23.287: Application layer support for V2X services;        Functional architecture and information flows    -   3GPP TS 23.501: System Architecture for the 5G System    -   3GPP TS 23.502: Procedures for the 5G System    -   3GPP TS 23.503: Policy and Charging Control Framework for the 5G        System; Stage 2    -   3GPP TS 24.501: Non-Access-Stratum (NAS) protocol for 5G System        (5GS); Stage 3    -   3GPP TS 24.502: Access to the 3GPP 5G Core Network (5GCN) via        non-3GPP access networks    -   3GPP TS 24.526: User Equipment (UE) policies for 5G System        (5GS); Stage 3

FIG. 2 is a diagram illustrating an exemplary method oftransmitting/receiving 3GPP signals.

Referring to FIG. 2, when a UE is powered on or enters a new cell, theUE performs an initial cell search involving acquisition ofsynchronization with a BS (S201). For the initial cell search, the UEreceives a primary synchronization channel (P-SCH) and a secondarysynchronization channel (S-SCH), acquires synchronization with the BS,and obtains information such as a cell identifier (ID) from the P-SCHand the S-SCH. In the LTE system and the NR system, the P-SCH and theS-SCH are referred to as a primary synchronization signal (PSS) and asecondary synchronization signal (SSS), respectively. The initial cellsearch procedure will be described below in greater detail.

After the initial cell search, the UE may receive a PBCH from the BS andacquire broadcast information within a cell from the PBCH. During theinitial cell search, the UE may check a DL channel state by receiving aDL RS.

Upon completion of the initial cell search, the UE may acquire morespecific system information by receiving a PDCCH and receiving a PDSCHaccording to information carried on the PDCCH (S202).

When the UE initially accesses the BS or has no radio resources forsignal transmission, the UE may perform a random access procedure withthe BS (S203 to S206). For this purpose, the UE may transmit apredetermined sequence as a preamble on a PRACH (S203 and S205) andreceive a PDCCH, and a random access response (RAR) message in responseto the preamble on a PDSCH corresponding to the PDCCH (S204 and S206).If the random access procedure is contention-based, the UE mayadditionally perform a contention resolution procedure. The randomaccess procedure will be described below in greater detail.

After the above procedure, the UE may then perform PDCCH/PDSCH reception(S207) and PUSCH/PUCCH transmission (S208) in a general UL/DL signaltransmission procedure. Particularly, the UE receives DCI on a PDCCH.

The UE monitors a set of PDCCH candidates in monitoring occasionsconfigured for one or more control element sets (CORESETs) in a servingcell according to a corresponding search space configuration. The set ofPDCCH candidates to be monitored by the UE is defined from theperspective of search space sets. A search space set may be a commonsearch space set or a UE-specific search space set. A CORESET includes aset of (physical) RBs that last for a time duration of one to three OFDMsymbols. The network may configure a plurality of CORESETs for the UE.The UE monitors PDCCH candidates in one or more search space sets.Herein, monitoring is attempting to decode PDCCH candidate(s) in asearch space. When the UE succeeds in decoding one of the PDCCHcandidates in the search space, the UE determines that a PDCCH has beendetected from among the PDCCH candidates and performs PDSCH reception orPUSCH transmission based on DCI included in the detected PDCCH.

The PDCCH may be used to schedule DL transmissions on a PDSCH and ULtransmissions on a PUSCH. DCI in the PDCCH includes a DL assignment(i.e., a DL grant) including at least a modulation and coding format andresource allocation information for a DL shared channel, and a UL grantincluding a modulation and coding format and resource allocationinformation for a UL shared channel.

Initial Access (IA) Procedure

Synchronization Signal Block (SSB) Transmission and Related Operation

FIG. 3 is a diagram illustrating an exemplary SSB structure. The UE mayperform cell search, system information acquisition, beam alignment forinitial access, DL measurement, and so on, based on an SSB. The term SSBis interchangeably used with synchronization signal/physical broadcastchannel (SS/PBCH).

Referring to FIG. 3, an SSB includes a PSS, an SSS, and a PBCH. The SSBincludes four consecutive OFDM symbols, and the PSS, the PBCH, theSSS/PBCH, or the PBCH is transmitted in each of the OFDM symbols. ThePBCH is encoded/decoded based on a polar code and modulated/demodulatedin quadrature phase shift keying (QPSK). The PBCH in an OFDM symbolincludes data REs to which a complex modulated value of the PBCH ismapped and DMRS REs to which a DMRS for the PBCH is mapped. There arethree DMRS REs per RB in an OFDM symbol and three data REs between everytwo of the DMRS REs.

Cell Search

Cell search is a process of acquiring the time/frequency synchronizationof a cell and detecting the cell ID (e.g., physical cell ID (PCI)) ofthe cell by a UE. The PSS is used to detect a cell ID in a cell IDgroup, and the SSS is used to detect the cell ID group. The PBCH is usedfor SSB (time) index detection and half-frame detection.

In the 5G system, there are 336 cell ID groups each including 3 cellIDs. Therefore, a total of 1008 cell IDs are available. Informationabout a cell ID group to which the cell ID of a cell belongs isprovided/acquired by/from the SSS of the cell, and information about thecell ID among 336 cells within the cell ID is provided/acquired by/fromthe PSS.

The SSB is periodically transmitted with an SSB periodicity. The UEassumes a default SSB periodicity of 20 ms during initial cell search.After cell access, the SSB periodicity may be set to one of {5 ms, 10ms, 20 ms, 40 ms, 80 ms, 160 ms} by the network (e.g., a BS). An SSBburst set is configured at the start of an SSB period. The SSB burst setis composed of a 5-ms time window (i.e., half-frame), and the SSB may betransmitted up to L times within the SSB burst set. The maximum number Lof SSB transmissions may be given as follows according to the frequencyband of a carrier.

-   -   For frequency range up to 3 GHz, L=4    -   For frequency range from 3 GHz to 6 GHz, L=8    -   For frequency range from 6 GHz to 52.6 GHz, L=64

The possible time positions of SSBs in a half-frame are determined by asubcarrier spacing, and the periodicity of half-frames carrying SSBs isconfigured by the network. The time positions of SSB candidates areindexed as 0 to L−1 (SSB indexes) in a time order in an SSB burst set(i.e., half-frame). Other SSBs may be transmitted in different spatialdirections (by different beams spanning the coverage area of the cell)during the duration of a half-frame. Accordingly, an SSB index (SSBI)may be associated with a BS transmission (Tx) beam in the 5G system.

The UE may acquire DL synchronization by detecting an SSB. The UE mayidentify the structure of an SSB burst set based on a detected (time)SSBI and hence a symbol/slot/half-frame boundary. The number of aframe/half-frame to which the detected SSB belongs may be identified byusing system frame number (SFN) information and half-frame indicationinformation.

Specifically, the UE may acquire the 10-bit SFN of a frame carrying thePBCH from the PBCH. Subsequently, the UE may acquire 1-bit half-frameindication information. For example, when the UE detects a PBCH with ahalf-frame indication bit set to 0, the UE may determine that an SSB towhich the PBCH belongs is in the first half-frame of the frame. When theUE detects a PBCH with a half-frame indication bit set to 1, the UE maydetermine that an SSB to which the PBCH belongs is in the secondhalf-frame of the frame. Finally, the UE may acquire the SSBI of the SSBto which the PBCH belongs based on a DMRS sequence and PBCH payloaddelivered on the PBCH.

System Information (SI) Acquisition

SI is divided into a master information block (MIB) and a plurality ofsystem information blocks (SIBs). The SI except for the MIB may bereferred to as remaining minimum system information (RMSI). For details,the following may be referred to.

-   -   The MIB includes information/parameters for monitoring a PDCCH        that schedules a PDSCH carrying systemInformationBlock1 (SIB1),        and transmitted on a PBCH of an SSB by a BS. For example, a UE        may determine from the MIB whether there is any CORESET for a        Type0-PDCCH common search space. The Type0-PDCCH common search        space is a kind of PDCCH search space and used to transmit a        PDCCH that schedules an SI message. In the presence of a        Type0-PDCCH common search space, the UE may determine (1) a        plurality of contiguous RBs and one or more consecutive symbols        included in a CORESET, and (ii) a PDCCH occasion (e.g., a        time-domain position at which a PDCCH is to be received), based        on information (e.g., pdcch-ConfigSIBl) included in the MIB.    -   SIB1 includes information related to availability and scheduling        (e.g., a transmission period and an SI-window size) of the        remaining SIBs (hereinafter, referred to SIBx where x is an        integer equal to or larger than 2). For example, SIB1 may        indicate whether SIBx is broadcast periodically or in an        on-demand manner upon user request. If SIBx is provided in the        on-demand manner, SIB1 may include information required for the        UE to transmit an SI request. A PDCCH that schedules SIB1 is        transmitted in the Type0-PDCCH common search space, and SIB1 is        transmitted on a PDSCH indicated by the PDCCH.    -   SIBx is included in an SI message and transmitted on a PDSCH.        Each SI message is transmitted within a periodic time window        (i.e., SI-window).

Random Access Procedure

The random access procedure serves various purposes. For example, therandom access procedure may be used for network initial access,handover, and UE-triggered UL data transmission. The UE may acquire ULsynchronization and UL transmission resources in the random accessprocedure. The random access procedure may be contention-based orcontention-free.

FIG. 4 is a diagram illustrating an exemplary random access procedure.Particularly, FIG. 4 illustrates a contention-based random accessprocedure.

First, a UE may transmit a random access preamble as a first message(Msg1) of the random access procedure on a PRACH. In the presentdisclosure, a random access procedure and a random access preamble arealso referred to as a RACH procedure and a RACH preamble, respectively.

A plurality of preamble formats are defined by one or more RACH OFDMsymbols and different cyclic prefixes (CPs) (and/or guard times). A RACHconfiguration for a cell is included in system information of the celland provided to the UE. The RACH configuration includes informationabout a subcarrier spacing, available preambles, a preamble format, andso on for a PRACH. The RACH configuration includes associationinformation between SSBs and RACH (time-frequency) resources, that is,association information between SSBIs and RACH (time-frequency)resources. The SSBIs are associated with Tx beams of a BS, respectively.The UE transmits a RACH preamble in RACH time-frequency resourcesassociated with a detected or selected SSB. The BS may identify apreferred BS Tx beam of the UE based on time-frequency resources inwhich the RACH preamble has been detected.

An SSB threshold for RACH resource association may be configured by thenetwork, and a RACH preamble transmission (i.e., PRACH transmission) orretransmission is performed based on an SSB in which an RSRP satisfyingthe threshold has been measured. For example, the UE may select one ofSSB(s) satisfying the threshold and transmit or retransmit the RACHpreamble in RACH resources associated with the selected SSB.

Upon receipt of the RACH preamble from the UE, the BS transmits an RARmessage (a second message (Msg2)) to the UE. A PDCCH that schedules aPDSCH carrying the RAR message is cyclic redundancy check (CRC)-maskedby an RA radio network temporary identifier (RNTI) (RA-RNTI) andtransmitted. When the UE detects the PDCCH masked by the RA-RNTI, the UEmay receive the RAR message on the PDSCH scheduled by DCI delivered onthe PDCCH. The UE determines whether RAR information for the transmittedpreamble, that is, Msg1 is included in the RAR message. The UE maydetermine whether random access information for the transmitted Msg1 isincluded by checking the presence or absence of the RACH preamble ID ofthe transmitted preamble. If the UE fails to receive a response to Msg1,the UE may transmit the RACH preamble a predetermined number of or fewertimes, while performing power ramping. The UE calculates the PRACHtransmission power of a preamble retransmission based on the latestpathloss and a power ramping counter.

Upon receipt of the RAR information for the UE on the PDSCH, the UE mayacquire timing advance information for UL synchronization, an initial ULgrant, and a UE temporary cell RNTI (C-RNTI). The timing advanceinformation is used to control a UL signal transmission timing. Toenable better alignment between PUSCH/PUCCH transmission of the UE and asubframe timing at a network end, the network (e.g., BS) may measure thetime difference between PUSCH/PUCCH/SRS reception and a subframe andtransmit the timing advance information based on the measured timedifference. The UE may perform a UL transmission as a third message(Msg3) of the RACH procedure on a PUSCH. Msg3 may include an RRCconnection request and a UE ID. The network may transmit a fourthmessage (Msg4) in response to Msg3, and Msg4 may be treated as acontention solution message on DL. As the UE receives Msg4, the UE mayenter an RRC_CONNECTED state.

The contention-free RACH procedure may be used for handover of the UE toanother cell or BS or performed when requested by a BS command. Thecontention-free RACH procedure is basically similar to thecontention-based RACH procedure. However, compared to thecontention-based RACH procedure in which a preamble to be used israndomly selected among a plurality of RACH preambles, a preamble to beused by the UE (referred to as a dedicated RACH preamble) is allocatedto the UE by the BS in the contention-free RACH procedure. Informationabout the dedicated RACH preamble may be included in an RRC message(e.g., a handover command) or provided to the UE by a PDCCH order. Whenthe RACH procedure starts, the UE transmits the dedicated RACH preambleto the BS. When the UE receives the RACH procedure from the BS, the RACHprocedure is completed.

DL and UL Transmission/Reception Operations

DL Transmission/Reception Operation

DL grants (also called DL assignments) may be classified into (1)dynamic grant and (2) configured grant. A dynamic grant is a datatransmission/reception method based on dynamic scheduling of a BS,aiming to maximize resource utilization.

The BS schedules a DL transmission by DCI. The UE receives the DCI forDL scheduling (i.e., including scheduling information for a PDSCH)(referred to as DL grant DCI) from the BS. The DCI for DL scheduling mayinclude, for example, the following information: a BWP indicator, afrequency-domain resource assignment, a time-domain resource assignment,and a modulation and coding scheme (MCS).

The UE may determine a modulation order, a target code rate, and a TBsize (TBS) for the PDSCH based on an MCS field in the DCI. The UE mayreceive the PDSCH in time-frequency resources according to thefrequency-domain resource assignment and the time-domain resourceassignment.

The DL configured grant is also called semi-persistent scheduling (SPS).The UE may receive an RRC message including a resource configuration forDL data transmission from the BS. In the case of DL SPS, an actual DLconfigured grant is provided by a PDCCH, and the DL SPS is activated ordeactivated by the PDCCH. When DL SPS is configured, the BS provides theUE with at least the following parameters by RRC signaling: a configuredscheduling RNTI (CS-RNTI) for activation, deactivation, andretransmission; and a periodicity. An actual DL grant (e.g., a frequencyresource assignment) for DL SPS is provided to the UE by DCI in a PDCCHaddressed to the CS-RNTI. If a specific field in the DCI of the PDCCHaddressed to the CS-RNTI is set to a specific value for schedulingactivation, SPS associated with the CS-RNTI is activated. The DCI of thePDCCH addressed to the CS-RNTI includes actual frequency resourceallocation information, an MCS index, and so on. The UE may receive DLdata on a PDSCH based on the SPS.

UL Transmission/Reception Operation

UL grants may be classified into (1) dynamic grant that schedules aPUSCH dynamically by UL grant DCI and (2) configured grant thatschedules a PUSCH semi-statically by RRC signaling.

FIG. 5 is a diagram illustrating exemplary UL transmissions according toUL grants. Particularly, FIG. 5(a) illustrates a UL transmissionprocedure based on a dynamic grant, and FIG. 5(b) illustrates a ULtransmission procedure based on a configured grant.

In the case of a UL dynamic grant, the BS transmits DCI including ULscheduling information to the UE. The UE receives DCI for UL scheduling(i.e., including scheduling information for a PUSCH) (referred to as ULgrant DCI) on a PDCCH. The DCI for UL scheduling may include, forexample, the following information: a BWP indicator, a frequency-domainresource assignment, a time-domain resource assignment, and an MCS. Forefficient allocation of UL radio resources by the BS, the UE maytransmit information about UL data to be transmitted to the BS, and theBS may allocate UL resources to the UE based on the information. Theinformation about the UL data to be transmitted is referred to as abuffer status report (BSR), and the BSR is related to the amount of ULdata stored in a buffer of the UE.

Referring to FIG. 5(a), the illustrated UL transmission procedure is fora UE which does not have UL radio resources available for BSRtransmission. In the absence of a UL grant available for UL datatransmission, the UE is not capable of transmitting a BSR on a PUSCH.Therefore, the UE should request resources for UL data, starting withtransmission of an SR on a PUCCH. In this case, a 5-step UL resourceallocation procedure is used.

Referring to FIG. 5(a), in the absence of PUSCH resources for BSRtransmission, the UE first transmits an SR to the BS, for PUSCH resourceallocation. The SR is used for the UE to request PUSCH resources for ULtransmission to the BS, when no PUSCH resources are available to the UEin spite of occurrence of a buffer status reporting event. In thepresence of valid PUCCH resources for the SR, the UE transmits the SR ona PUCCH, whereas in the absence of valid PUCCH resources for the SR, theUE starts the afore-described (contention-based) RACH procedure. Uponreceipt of a UL grant in UL grant DCI from the BS, the UE transmits aBSR to the BS in PUSCH resources allocated by the UL grant. The BSchecks the amount of UL data to be transmitted by the UE based on theBSR and transmits a UL grant in UL grant DCI to the UE. Upon detectionof a PDCCH including the UL grant DCI, the UE transmits actual UL datato the BS on a PUSCH based on the UL grant included in the UL grant DCI.

Referring to FIG. 5(b), in the case of a configured grant, the UEreceives an RRC message including a resource configuration for UL datatransmission from the BS. In the NR system, two types of UL configuredgrants are defined: type 1 and type 2. In the case of UL configuredgrant type 1, an actual UL grant (e.g., time resources and frequencyresources) is provided by RRC signaling, whereas in the case of ULconfigured grant type 2, an actual UL grant is provided by a PDCCH, andactivated or deactivated by the PDCCH. If configured grant type 1 isconfigured, the BS provides the UE with at least the followingparameters by RRC signaling: a CS-RNTI for retransmission; a periodicityof configured grant type 1; information about a starting symbol index Sand the number L of symbols for a PUSCH in a slot; a time-domain offsetrepresenting a resource offset with respect to SFN=0 in the time domain;and an MCS index representing a modulation order, a target code rate,and a TB size. If configured grant type 2 is configured, the BS providesthe UE with at least the following parameters by RRC signaling: aCS-RNTI for activation, deactivation, and retransmission; and aperiodicity of configured grant type 2. An actual UL grant of configuredgrant type 2 is provided to the UE by DCI of a PDCCH addressed to aCS-RNTI. If a specific field in the DCI of the PDCCH addressed to theCS-RNTI is set to a specific value for scheduling activation, configuredgrant type 2 associated with the CS-RNTI is activated. The DCI set to aspecific value for scheduling activation in the PDCCH includes actualfrequency resource allocation information, an MCS index, and so on. TheUE may perform a UL transmission on a PUSCH based on a configured grantof type 1 or type 2.

FIG. 6 is a conceptual diagram illustrating exemplary physical channelprocessing.

Each of the blocks illustrated in FIG. 6 may be performed in acorresponding module of a physical layer block in a transmission device.More specifically, the signal processing depicted in FIG. 6 may beperformed for UL transmission by a processor of a UE described in thepresent disclosure. Signal processing of FIG. 6 except for transformprecoding, with CP-OFDM signal generation instead of SC-FDMA signalgeneration may be performed for DL transmission in a processor of a BSdescribed in the present disclosure. Referring to FIG. 6, UL physicalchannel processing may include scrambling, modulation mapping, layermapping, transform precoding, precoding, RE mapping, and SC-FDMA signalgeneration. The above processes may be performed separately or togetherin the modules of the transmission device. The transform precoding, akind of discrete Fourier transform (DFT), is to spread UL data in aspecial manner that reduces the peak-to-average power ratio (PAPR) of awaveform. OFDM which uses a CP together with transform precoding for DFTspreading is referred to as DFT-s-OFDM, and OFDM using a CP without DFTspreading is referred to as CP-OFDM. An SC-FDMA signal is generated byDFT-s-OFDM. In the NR system, if transform precoding is enabled for UL,transform precoding may be applied optionally. That is, the NR systemsupports two options for a UL waveform: one is CP-OFDM and the other isDFT-s-OFDM. The BS provides RRC parameters to the UE such that the UEdetermines whether to use CP-OFDM or DFT-s-OFDM for a UL transmissionwaveform. FIG. 6 is a conceptual view illustrating UL physical channelprocessing for DFT-s-OFDM. For CP-OFDM, transform precoding is omittedfrom the processes of FIG. 6. For DL transmission, CP-OFDM is used forDL waveform transmission.

Each of the above processes will be described in greater detail. For onecodeword, the transmission device may scramble coded bits of thecodeword by a scrambler and then transmit the scrambled bits on aphysical channel. The codeword is obtained by encoding a TB. Thescrambled bits are modulated to complex-valued modulation symbols by amodulation mapper. The modulation mapper may modulate the scrambled bitsin a predetermined modulation scheme and arrange the modulated bits ascomplex-valued modulation symbols representing positions on a signalconstellation. Pi/2-binary phase shift keying (pi/2-BPSK), m-phase shiftkeying (m-PSK), m-quadrature amplitude modulation (m-QAM), or the likeis available for modulation of the coded data. The complex-valuedmodulation symbols may be mapped to one or more transmission layers by alayer mapper. A complexed-value modulation symbol on each layer may beprecoded by a precoder, for transmission through an antenna port. Iftransform precoding is possible for UL transmission, the precoder mayperform precoding after the complex-valued modulation symbols aresubjected to transform precoding, as illustrated in FIG. 6. The precodermay output antenna-specific symbols by processing the complex-valuedmodulation symbols in a multiple input multiple output (MIMO) schemeaccording to multiple Tx antennas, and distribute the antenna-specificsymbols to corresponding RE mappers. An output z of the precoder may beobtained by multiplying an output y of the layer mapper by an N×Mprecoding matrix, W where N is the number of antenna ports and M is thenumber of layers. The RE mappers map the complex-valued modulationsymbols for the respective antenna ports to appropriate REs in an RBallocated for transmission. The RE mappers may map the complex-valuedmodulation symbols to appropriate subcarriers, and multiplex the mappedsymbols according to users. SC-FDMA signal generators (CP-OFDM signalgenerators, when transform precoding is disabled in DL transmission orUL transmission) may generate complex-valued time domain OFDM symbolsignals by modulating the complex-valued modulation symbols in aspecific modulations scheme, for example, in OFDM. The SC-FDMA signalgenerators may perform inverse fast Fourier transform (IFFT) on theantenna-specific symbols and insert CPs into the time-domainIFFT-processed symbols. The OFDM symbols are subjected todigital-to-analog conversion, frequency upconversion, and so on, andthen transmitted to a reception device through the respective Txantennas. Each of the SC-FDMA signal generators may include an IFFTmodule, a CP inserter, a digital-to-analog converter (DAC), a frequencyupconverter, and so on.

A signal processing procedure of the reception device is performed in areverse order of the signal processing procedure of the transmissiondevice. For details, refer to the above description and FIG. 6.

Now, a description will be given of the PUCCH.

The PUCCH is used for UCI transmission. UCI includes an SR requesting ULtransmission resources, CSI representing a UE-measured DL channel statebased on a DL RS, and/or an HARQ-ACK indicating whether a UE hassuccessfully received DL data.

The PUCCH supports multiple formats, and the PUCCH formats areclassified according to symbol durations, payload sizes, andmultiplexing or non-multiplexing. [Table 1] below lists exemplary PUCCHformats.

TABLE 1 PUCCH length in Number Format OFDM symbols of bits Etc. 0 1-2 ≤2 Sequence selection 1 4-14 ≤2 Sequence modulation 2 1-2  >2 CP-OFDM 34-14 >2 DFT-s-OFDM (no UE multiplexing) 4 4-14 >2 DFT-s-OFDM (Pre DFTorthogonal cover code(OCC))

The BS configures PUCCH resources for the UE by RRC signaling. Forexample, to allocate PUCCH resources, the BS may configure a pluralityof PUCCH resource sets for the UE, and the UE may select a specificPUCCH resource set corresponding to a UCI (payload) size (e.g., thenumber of UCI bits). For example, the UE may select one of the followingPUCCH resource sets according to the number of UCI bits, N_(UCI).

-   -   PUCCH resource set #0, if the number of UCI bits ≤2    -   PUCCH resource set #1, if 2<the number of UCI bits ≤N₁    -   . . .    -   PUCCH resource set #(K−1), if NK-2<the number of UCI bits        ≤N_(k-1)

Herein, K represents the number of PUCCH resource sets (K>1), and Nirepresents the maximum number of UCI bits supported by PUCCH resourceset #i. For example, PUCCH resource set #1 may include resources ofPUCCH format 0 to PUCCH format 1, and the other PUCCH resource sets mayinclude resources of PUCCH format 2 to PUCCH format 4.

Subsequently, the BS may transmit DCI to the UE on a PDCCH, indicating aPUCCH resource to be used for UCI transmission among the PUCCH resourcesof a specific PUCCH resource set by an ACK/NACK resource indicator (ARI)in the DCI. The ARI may be used to indicate a PUCCH resource forHARQ-ACK transmission, also called a PUCCH resource indicator (PRI).

Enhanced Mobile Broadband Communication (eMBB)

In the NR system, a massive MIMO environment in which the number ofTx/Rx antennas is significantly increased is under consideration. On theother hand, in an NR system operating at or above 6 GHz, beamforming isconsidered, in which a signal is transmitted with concentrated energy ina specific direction, not omni-directionally, to compensate for rapidpropagation attenuation. Accordingly, there is a need for hybridbeamforming with analog beamforming and digital beamforming incombination according to a position to which a beamforming weightvector/precoding vector is applied, for the purpose of increasedperformance, flexible resource allocation, and easiness offrequency-wise beam control.

Hybrid Beamforming

FIG. 7 is a block diagram illustrating an exemplary transmitter andreceiver for hybrid beamforming.

In hybrid beamforming, a BS or a UE may form a narrow beam bytransmitting the same signal through multiple antennas, using anappropriate phase difference and thus increasing energy only in aspecific direction.

Beam Management (BM)

BM is a series of processes for acquiring and maintaining a set of BS(or transmission and reception point (TRP)) beams and/or UE beamsavailable for DL and UL transmissions/receptions. BM may include thefollowing processes and terminology.

-   -   Beam measurement: the BS or the UE measures the characteristics        of a received beamformed signal.    -   Beam determination: the BS or the UE selects its Tx beam/Rx        beam.    -   Beam sweeping: a spatial domain is covered by using a Tx beam        and/or an Rx beam in a predetermined method for a predetermined        time interval.    -   Beam report: the UE reports information about a signal        beamformed based on a beam measurement.

The BM procedure may be divided into (1) a DL BM procedure using an SSBor CSI-RS and (2) a UL BM procedure using an SRS. Further, each BMprocedure may include Tx beam sweeping for determining a Tx beam and Rxbeam sweeping for determining an Rx beam. The following description willfocus on the DL BM procedure using an SSB.

The DL BM procedure using an SSB may include (1) transmission of abeamformed SSB from the BS and (2) beam reporting of the UE. An SSB maybe used for both of Tx beam sweeping and Rx beam sweeping. SSB-based Rxbeam sweeping may be performed by attempting SSB reception whilechanging Rx beams at the UE.

SSB-based beam reporting may be configured, when CSI/beam is configuredin the RRC_CONNECTED state.

-   -   The UE receives information about an SSB resource set used for        BM from the BS. The SSB resource set may be configured with one        or more SSBIs. For each SSB resource set, SSBI 0 to SSBI 63 may        be defined.    -   The UE receives signals in SSB resources from the BS based on        the information about the SSB resource set.    -   When the BS configures the UE with an SSBRI and RSRP reporting,        the UE reports a (best) SSBRI and an RSRP corresponding to the        SSBRI to the BS.

The BS may determine a BS Tx beam for use in DL transmission to the UEbased on a beam report received from the UE.

Beam Failure Recovery (BFR) Procedure

In a beamforming system, radio link failure (RLF) may often occur due torotation or movement of a UE or beamforming blockage. Therefore, BFR issupported to prevent frequent occurrence of RLF in NR.

For beam failure detection, the BS configures beam failure detection RSsfor the UE. If the number of beam failure indications from the physicallayer of the UE reaches a threshold configured by RRC signaling within aperiod configured by RRC signaling of the BS, the UE declares beamfailure.

After the beam failure is detected, the UE triggers BFR by initiating aRACH procedure on a Pcell, and performs BFR by selecting a suitable beam(if the BS provides dedicated RACH resources for certain beams, the UEperforms the RACH procedure for BFR by using the dedicated RACHresources first of all). Upon completion of the RACH procedure, the UEconsiders that the BFR has been completed.

Ultra-Reliable and Low Latency Communication (URLLC)

A URLLC transmission defined in NR may mean a transmission with (1) arelatively small traffic size, (2) a relatively low arrival rate, (3) anextremely low latency requirement (e.g., 0.5 ms or 1 ms), (4) arelatively short transmission duration (e.g., 2 OFDM symbols), and (5)an emergency service/message.

Pre-Emption Indication

Although eMBB and URLLC services may be scheduled in non-overlappedtime/frequency resources, a URLLC transmission may take place inresources scheduled for on-going eMBB traffic. To enable a UE receivinga PDSCH to determine that the PDSCH has been partially punctured due toURLLC transmission of another UE, a preemption indication may be used.The preemption indication may also be referred to as an interruptedtransmission indication.

In relation to a preemption indication, the UE receives DL preemptionRRC information (e.g., a DownlinkPreemption IE) from the BS by RRCsignaling.

The UE receives DCI format 2_1 based on the DL preemption RRCinformation from the BS. For example, the UE attempts to detect a PDCCHconveying preemption indication-related DCI, DCI format 2_1 by using anint-RNTI configured by the DL preemption RRC information.

Upon detection of DCI format 2_1 for serving cell(s) configured by theDL preemption RRC information, the UE may assume that there is notransmission directed to the UE in RBs and symbols indicated by DCIformat 2_1 in a set of RBs and a set of symbols during a monitoringinterval shortly previous to a monitoring interval to which DCI format2_1 belongs. For example, the UE decodes data based on signals receivedin the remaining resource areas, considering that a signal in atime-frequency resource indicated by a preemption indication is not a DLtransmission scheduled for the UE.

Massive MTC (mMTC)

mMTC is one of 5G scenarios for supporting a hyper-connectivity servicein which communication is conducted with multiple UEs at the same time.In this environment, a UE intermittently communicates at a very lowtransmission rate with low mobility. Accordingly, mMTC mainly seeks longoperation of a UE with low cost. In this regard, MTC and narrowband-Internet of things (NB-IoT) handled in the 3GPP will be describedbelow.

The following description is given with the appreciation that atransmission time interval (TTI) of a physical channel is a subframe.For example, a minimum time interval between the start of transmissionof a physical channel and the start of transmission of the next physicalchannel is one subframe. However, a subframe may be replaced with aslot, a mini-slot, or multiple slots in the following description.

Machine Type Communication (MTC)

MTC is an application that does not require high throughput, applicableto machine-to-machine (M2M) or IoT. MTC is a communication technologywhich the 3GPP has adopted to satisfy the requirements of the IoTservice.

While the following description is given mainly of features related toenhanced MTC (eMTC), the same thing is applicable to MTC, eMTC, and MTCto be applied to 5G (or NR), unless otherwise mentioned. The term MTC asused herein may be interchangeable with eMTC, LTE-M1/M2, bandwidthreduced low complexity (BL)/coverage enhanced (CE), non-BL UE (inenhanced coverage), NR MTC, enhanced BL/CE, and so on.

MTC General

(1) MTC operates only in a specific system BW (or channel BW).

MTC may use a predetermined number of RBs among the RBs of a system bandin the legacy LTE system or the NR system. The operating frequency BW ofMTC may be defined in consideration of a frequency range and asubcarrier spacing in NR. A specific system or frequency BW in which MTCoperates is referred to as an MTC narrowband (NB) or MTC subband. In NR,MTC may operate in at least one BWP or a specific band of a BWP.

While MTC is supported by a cell having a much larger BW (e.g., 10 MHz)than 1.08 MHz, a physical channel and signal transmitted/received in MTCis always limited to 1.08 MHz or 6 (LTE) RBs. For example, a narrowbandis defined as 6 non-overlapped consecutive physical resource blocks(PRBs) in the frequency domain in the LTE system.

In MTC, some DL and UL channels are allocated restrictively within anarrowband, and one channel does not occupy a plurality of narrowbandsin one time unit. FIG. 8(a) is a diagram illustrating an exemplarynarrowband operation, and FIG. 8(b) is a diagram illustrating exemplaryMTC channel repetition with RF retuning.

An MTC narrowband may be configured for a UE by system information orDCI transmitted by a BS.

(2) MTC does not use a channel (defined in legacy LTE or NR) which is tobe distributed across the total system BW of the legacy LTE or NR. Forexample, because a legacy LTE PDCCH is distributed across the totalsystem BW, the legacy PDCCH is not used in MTC. Instead, a new controlchannel, MTC PDCCH (MPDCCH) is used in MTC. The MPDCCH istransmitted/received in up to 6 RBs in the frequency domain. In the timedomain, the MPDCCH may be transmitted in one or more OFDM symbolsstarting with an OFDM symbol of a starting OFDM symbol index indicatedby an RRC parameter from the BS among the OFDM symbols of a subframe.

(3) In MTC, PBCH, PRACH, MPDCCH, PDSCH, PUCCH, and PUSCH may betransmitted repeatedly. The MTC repeated transmissions may make thesechannels decodable even when signal quality or power is very poor as ina harsh condition like basement, thereby leading to the effect of anincreased cell radius and signal penetration.

MTC Operation Modes and Levels

For CE, two operation modes, CE Mode A and CE Mode B and four differentCE levels are used in MTC, as listed in [Table 2] below.

TABLE 2 Mode Level Description Mode A Level 1 No repetition for PRACHLevel 2 Small Number of Repetition for PRACH Mode B Level 3 MediumNumber of Repetition for PRACH Level 4 Large Number of Repetition forPRACH

An MTC operation mode is determined by a BS and a CE level is determinedby an MTC UE.

MTC Guard Period

The position of a narrowband used for MTC may change in each specifictime unit (e.g., subframe or slot). An MTC UE may tune to differentfrequencies in different time units. A certain time may be required forfrequency retuning and thus used as a guard period for MTC. Notransmission and reception take place during the guard period.

MTC Signal Transmission/Reception Method

Apart from features inherent to MTC, an MTC signaltransmission/reception procedure is similar to the procedure illustratedin FIG. 2. The operation of S201 in FIG. 2 may also be performed forMTC. A PSS/SSS used in an initial cell search operation in MTC may bethe legacy LTE PSS/SSS.

After acquiring synchronization with a BS by using the PSS/SSS, an MTCUE may acquire broadcast information within a cell by receiving a PBCHsignal from the BS. The broadcast information transmitted on the PBCH isan MIB. In MTC, reserved bits among the bits of the legacy LTE MIB areused to transmit scheduling information for a new system informationblock 1 bandwidth reduced (SIB1-BR). The scheduling information for theSIB1-BR may include information about a repetition number and a TBS fora PDSCH conveying SIB1-BR. A frequency resource assignment for the PDSCHconveying SIB-BR may be a set of 6 consecutive RBs within a narrowband.The SIB-BR is transmitted directly on the PDSCH without a controlchannel (e.g., PDCCH or MPDCCH) associated with SIB-BR.

After completing the initial cell search, the MTC UE may acquire morespecific system information by receiving an MPDCCH and a PDSCH based oninformation of the MPDCCH (S202).

Subsequently, the MTC UE may perform a RACH procedure to completeconnection to the BS (S203 to S206). A basic configuration for the RACHprocedure of the MTC UE may be transmitted in SIB2. Further, SIB2includes paging-related parameters. In the 3GPP system, a pagingoccasion (PO) means a time unit in which a UE may attempt to receivepaging. Paging refers to the network's indication of the presence ofdata to be transmitted to the UE. The MTC UE attempts to receive anMPDCCH based on a P-RNTI in a time unit corresponding to its PO in anarrowband configured for paging, paging narrowband (PNB). When the UEsucceeds in decoding the MPDCCH based on the P-RNTI, the UE may checkits paging message by receiving a PDSCH scheduled by the MPDCCH. In thepresence of its paging message, the UE accesses the network byperforming the RACH procedure.

In MTC, signals and/or messages (Msg1, Msg2, Msg3, and Msg4) may betransmitted repeatedly in the RACH procedure, and a different repetitionpattern may be set according to a CE level.

For random access, PRACH resources for different CE levels are signaledby the BS. Different PRACH resources for up to 4 respective CE levelsmay be signaled to the MTC UE. The MTC UE measures an RSRP using a DL RS(e.g., CRS, CSI-RS, or TRS) and determines one of the CE levels signaledby the BS based on the measurement. The UE selects one of differentPRACH resources (e.g., frequency, time, and preamble resources for aPARCH) for random access based on the determined CE level and transmitsa PRACH. The BS may determine the CE level of the UE based on the PRACHresources that the UE has used for the PRACH transmission. The BS maydetermine a CE mode for the UE based on the CE level that the UEindicates by the PRACH transmission. The BS may transmit DCI to the UEin the CE mode.

Search spaces for an RAR for the PRACH and contention resolutionmessages are signaled in system information by the BS.

After the above procedure, the MTC UE may receive an MPDCCH signaland/or a PDSCH signal (S207) and transmit a PUSCH signal and/or a PUCCHsignal (S208) in a general UL/DL signal transmission procedure. The MTCUE may transmit UCI on a PUCCH or a PUSCH to the BS.

Once an RRC connection for the MTC UE is established, the MTC UEattempts to receive an MDCCH by monitoring an MPDCCH in a configuredsearch space in order to acquire UL and DL data allocations.

In legacy LTE, a PDSCH is scheduled by a PDCCH. Specifically, the PDCCHmay be transmitted in the first N (N=1, 2 or 3) OFDM symbols of asubframe, and the PDSCH scheduled by the PDCCH is transmitted in thesame subframe.

Compared to legacy LTE, an MPDCCH and a PDSCH scheduled by the MPDCCHare transmitted/received in different subframes in MTC. For example, anMPDCCH with a last repetition in subframe #n schedules a PDSCH startingin subframe #n+2. The MPDCCH may be transmitted only once or repeatedly.A maximum repetition number of the MPDCCH is configured for the UE byRRC signaling from the BS. DCI carried on the MPDCCH providesinformation on how many times the MPDCCH is repeated so that the UE maydetermine when the PDSCH transmission starts. For example, if DCI in anMPDCCH starting in subframe #n includes information indicating that theMPDCCH is repeated 10 times, the MPDCCH may end in subframe #n+9 and thePDSCH may start in subframe #n+ll. The DCI carried on the MPDCCH mayinclude information about a repetition number for a physical datachannel (e.g., PUSCH or PDSCH) scheduled by the DCI. The UE maytransmit/receive the physical data channel repeatedly in the time domainaccording to the information about the repetition number of the physicaldata channel scheduled by the DCI. The PDSCH may be scheduled in thesame or different narrowband as or from a narrowband in which the MPDCCHscheduling the PDSCH is transmitted. When the MPDCCH and the PDSCH arein different narrowbands, the MTC UE needs to retune to the frequency ofthe narrowband carrying the PDSCH before decoding the PDSCH. For ULscheduling, the same timing as in legacy LTE may be followed. Forexample, an MPDCCH ending in subframe #n may schedule a PUSCHtransmission starting in subframe #n+4. If a physical channel isrepeatedly transmitted, frequency hopping is supported between differentMTC subbands by RF retuning. For example, if a PDSCH is repeatedlytransmitted in 32 subframes, the PDSCH is transmitted in the first 16subframes in a first MTC subband, and in the remaining 16 subframes in asecond MTC subband. MTC may operate in half-duplex mode.

Narrowband-Internet of Things (NB-IoT)

NB-IoT may refer to a system for supporting low complexity, low powerconsumption, and efficient use of frequency resources by a system BWcorresponding to one RB of a wireless communication system (e.g., theLTE system or the NR system). NB-IoT may operate in half-duplex mode.NB-IoT may be used as a communication scheme for implementing IoT bysupporting, for example, an MTC device (or UE) in a cellular system.

In NB-IoT, each UE perceives one RB as one carrier. Therefore, an RB anda carrier as mentioned in relation to NB-IoT may be interpreted as thesame meaning.

While a frame structure, physical channels, multi-carrier operations,and general signal transmission/reception in relation to NB-IoT will bedescribed below in the context of the legacy LTE system, the descriptionis also applicable to the next generation system (e.g., the NR system).Further, the description of NB-IoT may also be applied to MTC servingsimilar technical purposes (e.g., low power, low cost, and coverageenhancement).

NB-IoT Frame Structure and Physical Resources

A different NB-IoT frame structure may be configured according to asubcarrier spacing. For example, for a subcarrier spacing of 15 kHz, theNB-IoT frame structure may be identical to that of a legacy system(e.g., the LTE system). For example, a 10-ms NB-IoT frame may include 10l-ms NB-IoT subframes each including two 0.5-ms slots. Each 0.5-msNB-IoT slot may include 7 OFDM symbols. In another example, for a BWP orcell/carrier having a subcarrier spacing of 3.75 kHz, a 10-ms NB-IoTframe may include five 2-ms NB-IoT subframes each including 7 OFDMsymbols and one guard period (GP). Further, a 2-ms NB-IoT subframe maybe represented in NB-IoT slots or NB-IoT resource units (RUs). TheNB-IoT frame structures are not limited to the subcarrier spacings of 15kHz and 3.75 kHz, and NB-IoT for other subcarrier spacings (e.g., 30kHz) may also be considered by changing time/frequency units.

NB-IoT DL physical resources may be configured based on physicalresources of other wireless communication systems (e.g., the LTE systemor the NR system) except that a system BW is limited to a predeterminednumber of RBs (e.g., one RB, that is, 180 kHz). For example, if theNB-IoT DL supports only the 15-kHz subcarrier spacing as describedbefore, the NB-IoT DL physical resources may be configured as a resourcearea in which the resource grid illustrated in FIG. 1 is limited to oneRB in the frequency domain.

Like the NB-IoT DL physical resources, NB-IoT UL resources may also beconfigured by limiting a system BW to one RB. In NB-IoT, the number ofUL subcarriers N^(UL) _(sc) and a slot duration T_(slot) may be given asillustrated in [Table 3] below. In NB-IoT of the LTE system, theduration of one slot, T_(slot) is defined by 7 SC-FDMA symbols in thetime domain.

TABLE 3 Subcarrier spacing N^(UL) _(sc) T_(slot) Δf = 3.75 kHz 48  6144· T_(s) Δf = 15 kHz 12 15360 · T_(s)

In NB-IoT, RUs are used for mapping to REs of a PUSCH for NB-IoT(referred to as an NPUSCH). An RU may be defined by N^(UL)_(symb)*N^(UL) _(slot) SC-FDMA symbols in the time domain by N^(RU)_(sc) consecutive subcarriers in the frequency domain. For example,N^(RU) _(SC) and N^(UL) _(symb) are listed in [Table 4] for acell/carrier having an FDD frame structure and in [Table 5] for acell/carrier having a TDD frame structure.

TABLE 4 NPUSCH format Δf N^(RU) _(sc) N^(UL) _(slots) N^(UL) _(symb) 13.75 kHz 1 16 7 15 kHz 1 16 3 8 6 4 12 2 2 3.75 kHz 1 4 15 kHz 1 4

TABLE 5 Supported NPUSCH uplink-downlink format Δf configurations N^(RU)_(sc) N^(UL) _(slots) N^(UL) _(symb) 1 3.75 kHz 1, 4 1 16 7 15 kHz 1, 2,3, 4, 5 1 16 3 8 6 4 12 2 2 3.75 kHz 1, 4 1 4 15 kHz 1, 2, 3, 4, 5 1 4

NB-IoT Physical Channels

OFDMA may be adopted for NB-IoT DL based on the 15-kHz subcarrierspacing. Because OFDMA provides orthogonality between subcarriers,co-existence with other systems (e.g., the LTE system or the NR system)may be supported efficiently. The names of DL physical channels/signalsof the NB-IoT system may be prefixed with “N (narrowband)” to bedistinguished from their counterparts in the legacy system. For example,DL physical channels may be named NPBCH, NPDCCH, NPDSCH, and so on, andDL physical signals may be named NPSS, NSSS, narrowband reference signal(NRS), narrowband positioning reference signal (NPRS), narrowband wakeup signal (NWUS), and so on. The DL channels, NPBCH, NPDCCH, NPDSCH, andso on may be repeatedly transmitted to enhance coverage in the NB-IoTsystem. Further, new defined DCI formats may be used in NB-IoT, such asDCI format NO, DCI format N1, and DCI format N2.

SC-FDMA may be applied with the 15-kHz or 3.75-kHz subcarrier spacing toNB-IoT UL. As described in relation to DL, the names of physicalchannels of the NB-IoT system may be prefixed with “N (narrowband)” tobe distinguished from their counterparts in the legacy system. Forexample, UL channels may be named NPRACH, NPUSCH, and so on, and ULphysical signals may be named NDMRS and so on. NPUSCHs may be classifiedinto NPUSCH format 1 and NPUSCH format 2. For example, NPUSCH format 1may be used to transmit (or deliver) an uplink shared channel (UL-SCH),and NPUSCH format 2 may be used for UCI transmission such as HARQ ACKsignaling. A UL channel, NPRACH in the NB-IoT system may be repeatedlytransmitted to enhance coverage. In this case, the repeatedtransmissions may be subjected to frequency hopping.

Multi-Carrier Operation in NB-IoT

NB-IoT may be implemented in multi-carrier mode. A multi-carrieroperation may refer to using multiple carriers configured for differentusages (i.e., multiple carriers of different types) intransmitting/receiving channels and/or signals between a BS and a UE.

In the multi-carrier mode in NB-IoT, carriers may be divided into anchortype carrier (i.e., anchor carrier or anchor PRB) and non-anchor typecarrier (i.e., non-anchor carrier or non-anchor PRB).

The anchor carrier may refer to a carrier carrying an NPSS, an NSSS, andan NPBCH for initial access, and an NPDSCH for a system informationblock, N-SIB from the perspective of a BS. That is, a carrier forinitial access is referred to as an anchor carrier, and the othercarrier(s) is referred to as a non-anchor carrier in NB-IoT.

NB-IoT Signal Transmission/Reception Process

In NB-IoT, a signal is transmitted/received in a similar manner to theprocedure illustrated in FIG. 2, except for features inherent to NB-IoT.Referring to FIG. 2, when an NB-IoT UE is powered on or enters a newcell, the NB-IoT UE may perform an initial cell search (S201). For theinitial cell search, the NB-IoT UE may acquire synchronization with a BSand obtain information such as a cell ID by receiving an NPSS and anNSSS from the BS. Further, the NB-IoT UE may acquire broadcastinformation within a cell by receiving an NPBCH from the BS.

Upon completion of the initial cell search, the NB-IoT UE may acquiremore specific system information by receiving an NPDCCH and receiving anNPDSCH corresponding to the NPDCCH (S202). In other words, the BS maytransmit more specific system information to the NB-IoT UE which hascompleted the initial call search by transmitting an NPDCCH and anNPDSCH corresponding to the NPDCCH.

The NB-IoT UE may then perform a RACH procedure to complete a connectionsetup with the BS (S203 to S206). For this purpose, the NB-IoT UE maytransmit a preamble on an NPRACH to the BS (S203). As described before,it may be configured that the NPRACH is repeatedly transmitted based onfrequency hopping, for coverage enhancement. In other words, the BS may(repeatedly) receive the preamble on the NPRACH from the NB-IoT UE. TheNB-IoT UE may then receive an NPDCCH, and a RAR in response to thepreamble on an NPDSCH corresponding to the NPDCCH from the BS (S204). Inother words, the BS may transmit the NPDCCH, and the RAR in response tothe preamble on the NPDSCH corresponding to the NPDCCH to the NB-IoT UE.Subsequently, the NB-IoT UE may transmit an NPUSCH to the BS, usingscheduling information in the RAR (S205) and perform a contentionresolution procedure by receiving an NPDCCH and an NPDSCH correspondingto the NPDCCH (S206).

After the above process, the NB-IoT UE may perform an NPDCCH/NPDSCHreception (S207) and an NPUSCH transmission (S208) in a general UL/DLsignal transmission procedure. In other words, after the above process,the BS may perform an NPDCCH/NPDSCH transmission and an NPUSCH receptionwith the NB-IoT UE in the general UL/DL signal transmission procedure.

In NB-IoT, the NPBCH, the NPDCCH, and the NPDSCH may be transmittedrepeatedly, for coverage enhancement. A UL-SCH (i.e., general UL data)and UCI may be delivered on the PUSCH in NB-IoT. It may be configuredthat the UL-SCH and the UCI are transmitted in different NPUSCH formats(e.g., NPUSCH format 1 and NPUSCH format 2).

In NB-IoT, UCI may generally be transmitted on an NPUSCH. Further, theUE may transmit the NPUSCH periodically, aperiodically, orsemi-persistently according to request/indication of the network (e.g.,BS).

Wireless Communication Apparatus

FIG. 9 is a block diagram of an exemplary wireless communication systemto which proposed methods of the present disclosure are applicable.

Referring to FIG. 9, the wireless communication system includes a firstcommunication device 910 and/or a second communication device 920. Thephrases “A and/or B” and “at least one of A or B” are may be interpretedas the same meaning. The first communication device 910 may be a BS, andthe second communication device 920 may be a UE (or the firstcommunication device 910 may be a UE, and the second communicationdevice 920 may be a BS).

Each of the first communication device 910 and the second communicationdevice 920 includes a processor 911 or 921, a memory 914 or 924, one ormore Tx/Rx RF modules 915 or 925, a Tx processor 912 or 922, an Rxprocessor 913 or 923, and antennas 916 or 926. A Tx/Rx module may alsobe called a transceiver. The processor performs the afore-describedfunctions, processes, and/or methods. More specifically, on DL(communication from the first communication device 910 to the secondcommunication device 920), a higher-layer packet from a core network isprovided to the processor 911. The processor 911 implements Layer 2(i.e., L2) functionalities. On DL, the processor 911 is responsible formultiplexing between a logical channel and a transport channel,provisioning of a radio resource assignment to the second communicationdevice 920, and signaling to the second communication device 920. The Txprocessor 912 executes various signal processing functions of L1 (i.e.,the physical layer). The signal processing functions facilitate forwarderror correction (FEC) of the second communication device 920, includingcoding and interleaving. An encoded and interleaved signal is modulatedto complex-valued modulation symbols after scrambling and modulation.For the modulation, BPSK, QPSK, 16QAM, 64QAM, 246QAM, and so on areavailable according to channels. The complex-valued modulation symbols(hereinafter, referred to as modulation symbols) are divided intoparallel streams. Each stream is mapped to OFDM subcarriers andmultiplexed with an RS in the time and/or frequency domain. A physicalchannel is generated to carry a time-domain OFDM symbol stream bysubjecting the mapped signals to IFFT. The OFDM symbol stream isspatially precoded to multiple spatial streams. Each spatial stream maybe provided to a different antenna 916 through an individual Tx/Rxmodule (or transceiver) 915. Each Tx/Rx module 915 may upconvert thefrequency of each spatial stream to an RF carrier, for transmission. Inthe second communication device 920, each Tx/Rx module (or transceiver)925 receives a signal of the RF carrier through each antenna 926. EachTx/Rx module 925 recovers the signal of the RF carrier to a basebandsignal and provides the baseband signal to the Rx processor 923. The Rxprocessor 923 executes various signal processing functions of L1 (i.e.,the physical layer). The Rx processor 923 may perform spatial processingon information to recover any spatial stream directed to the secondcommunication device 920. If multiple spatial streams are directed tothe second communication device 920, multiple Rx processors may combinethe multiple spatial streams into a single OFDMA symbol stream. The Rxprocessor 923 converts an OFDM symbol stream being a time-domain signalto a frequency-domain signal by FFT. The frequency-domain signalincludes an individual OFDM symbol stream on each subcarrier of an OFDMsignal. Modulation symbols and an RS on each subcarrier are recoveredand demodulated by determining most likely signal constellation pointstransmitted by the first communication device 910. These soft decisionsmay be based on channel estimates. The soft decisions are decoded anddeinterleaved to recover the original data and control signaltransmitted on physical channels by the first communication device 910.The data and control signal are provided to the processor 921.

On UL (communication from the second communication device 920 to thefirst communication device 910), the first communication device 910operates in a similar manner as described in relation to the receiverfunction of the second communication device 920. Each Tx/Rx module 925receives a signal through an antenna 926. Each Tx/Rx module 925 providesan RF carrier and information to the Rx processor 923. The processor 921may be related to the memory 924 storing a program code and data. Thememory 924 may be referred to as a computer-readable medium.

Artificial Intelligence (AI)

Artificial intelligence is a field of studying AI or methodologies forcreating AI, and machine learning is a field of defining various issuesdealt with in the AI field and studying methodologies for addressing thevarious issues. Machine learning is defined as an algorithm thatincreases the performance of a certain operation through steadyexperiences for the operation.

An artificial neural network (ANN) is a model used in machine learningand may generically refer to a model having a problem-solving ability,which is composed of artificial neurons (nodes) forming a network viasynaptic connections. The ANN may be defined by a connection patternbetween neurons in different layers, a learning process for updatingmodel parameters, and an activation function for generating an outputvalue.

The ANN may include an input layer, an output layer, and optionally, oneor more hidden layers. Each layer includes one or more neurons, and theANN may include a synapse that links between neurons. In the ANN, eachneuron may output the function value of the activation function, for theinput of signals, weights, and deflections through the synapse.

Model parameters refer to parameters determined through learning andinclude a weight value of a synaptic connection and deflection ofneurons. A hyperparameter means a parameter to be set in the machinelearning algorithm before learning, and includes a learning rate, arepetition number, a mini batch size, and an initialization function.

The purpose of learning of the ANN may be to determine model parametersthat minimize a loss function. The loss function may be used as an indexto determine optimal model parameters in the learning process of theANN.

Machine learning may be classified into supervised learning,unsupervised learning, and reinforcement learning according to learningmethods.

Supervised learning may be a method of training an ANN in a state inwhich a label for training data is given, and the label may mean acorrect answer (or result value) that the ANN should infer with respectto the input of training data to the ANN. Unsupervised learning may be amethod of training an ANN in a state in which a label for training datais not given. Reinforcement learning may be a learning method in whichan agent defined in a certain environment is trained to select abehavior or a behavior sequence that maximizes cumulative compensationin each state.

Machine learning, which is implemented by a deep neural network (DNN)including a plurality of hidden layers among ANNs, is also referred toas deep learning, and deep learning is part of machine learning. Thefollowing description is given with the appreciation that machinelearning includes deep learning.

<Robot>

A robot may refer to a machine that automatically processes or executesa given task by its own capabilities. Particularly, a robot equippedwith a function of recognizing an environment and performing anoperation based on its decision may be referred to as an intelligentrobot.

Robots may be classified into industrial robots, medical robots,consumer robots, military robots, and so on according to their usages orapplication fields.

A robot may be provided with a driving unit including an actuator or amotor, and thus perform various physical operations such as moving robotjoints. Further, a movable robot may include a wheel, a brake, apropeller, and the like in a driving unit, and thus travel on the groundor fly in the air through the driving unit.

<Self-Driving>

Self-driving refers to autonomous driving, and a self-driving vehiclerefers to a vehicle that travels with no user manipulation or minimumuser manipulation.

For example, self-driving may include a technology of maintaining a lanewhile driving, a technology of automatically adjusting a speed, such asadaptive cruise control, a technology of automatically traveling along apredetermined route, and a technology of automatically setting a routeand traveling along the route when a destination is set.

Vehicles may include a vehicle having only an internal combustionengine, a hybrid vehicle having both an internal combustion engine andan electric motor, and an electric vehicle having only an electricmotor, and may include not only an automobile but also a train, amotorcycle, and the like.

Herein, a self-driving vehicle may be regarded as a robot having aself-driving function.

<eXtended Reality (XR)>

Extended reality is a generical term covering virtual reality (VR),augmented reality (AR), and mixed reality (MR). VR provides a real-worldobject and background only as a computer graphic (CG) image, AR providesa virtual CG image on a real object image, and MR is a computer graphictechnology that mixes and combines virtual objects into the real world.

MR is similar to AR in that the real object and the virtual object areshown together. However, in AR, the virtual object is used as acomplement to the real object, whereas in MR, the virtual object and thereal object are handled equally.

XR may be applied to a head-mounted display (HMD), a head-up display(HUD), a portable phone, a tablet PC, a laptop computer, a desktopcomputer, a TV, a digital signage, and so on. A device to which XR isapplied may be referred to as an XR device.

FIG. 10 illustrates an AI device 1000 according to an embodiment of thepresent disclosure.

The AI device 1000 illustrated in FIG. 10 may be configured as astationary device or a mobile device, such as a TV, a projector, aportable phone, a smartphone, a desktop computer, a laptop computer, adigital broadcasting terminal, a personal digital assistant (PDA), aportable multimedia player (PMP), a navigation device, a tablet PC, awearable device, a set-top box (STB), a digital multimedia broadcasting(DMB) receiver, a radio, a washing machine, a refrigerator, a digitalsignage, a robot, or a vehicle.

Referring to FIG. 10, the AI device 1000 may include a communicationunit 1010, an input unit 1020, a learning processor 1030, a sensing unit1040, an output unit 1050, a memory 1070, and a processor 1080.

The communication unit 1010 may transmit and receive data to and from anexternal device such as another AI device or an AI server by wired orwireless communication. For example, the communication unit 1010 maytransmit and receive sensor information, a user input, a learning model,and a control signal to and from the external device.

Communication schemes used by the communication unit 1010 include globalsystem for mobile communication (GSM), CDMA, LTE, 5G wireless local areanetwork (WLAN), wireless fidelity (Wi-Fi), Bluetooth™, radio frequencyidentification (RFID), infrared data association (IrDA), ZigBee, nearfield communication (NFC), and so on. Particularly, the 5G technologydescribed before with reference to FIGS. 1 to 9 may also be applied.

The input unit 1020 may acquire various types of data. The input unit1020 may include a camera for inputting a video signal, a microphone forreceiving an audio signal, and a user input unit for receivinginformation from a user. The camera or the microphone may be treated asa sensor, and thus a signal acquired from the camera or the microphonemay be referred to as sensing data or sensor information.

The input unit 1020 may acquire training data for model training andinput data to be used to acquire an output by using a learning model.The input unit 1020 may acquire raw input data. In this case, theprocessor 1080 or the learning processor 1030 may extract an inputfeature by preprocessing the input data.

The learning processor 1030 may train a model composed of an ANN byusing training data. The trained ANN may be referred to as a learningmodel. The learning model may be used to infer a result value for newinput data, not training data, and the inferred value may be used as abasis for determination to perform a certain operation.

The learning processor 1030 may perform AI processing together with alearning processor of an AI server.

The learning processor 1030 may include a memory integrated orimplemented in the AI device 1000. Alternatively, the learning processor1030 may be implemented by using the memory 1070, an external memorydirectly connected to the AI device 1000, or a memory maintained in anexternal device.

The sensing unit 1040 may acquire at least one of internal informationabout the AI device 1000, ambient environment information about the AIdevice 1000, and user information by using various sensors.

The sensors included in the sensing unit 1040 may include a proximitysensor, an illumination sensor, an accelerator sensor, a magneticsensor, a gyro sensor, an inertial sensor, a red, green, blue (RGB)sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonicsensor, an optical sensor, a microphone, a light detection and ranging(LiDAR), and a radar.

The output unit 1050 may generate a visual, auditory, or haptic output.

Accordingly, the output unit 1050 may include a display unit foroutputting visual information, a speaker for outputting auditoryinformation, and a haptic module for outputting haptic information.

The memory 1070 may store data that supports various functions of the AIdevice 1000. For example, the memory 1070 may store input data acquiredby the input unit 1020, training data, a learning model, a learninghistory, and so on.

The processor 1080 may determine at least one executable operation ofthe AI device 100 based on information determined or generated by a dataanalysis algorithm or a machine learning algorithm. The processor 1080may control the components of the AI device 1000 to execute thedetermined operation.

To this end, the processor 1080 may request, search, receive, or utilizedata of the learning processor 1030 or the memory 1070. The processor1080 may control the components of the AI device 1000 to execute apredicted operation or an operation determined to be desirable among theat least one executable operation.

When the determined operation needs to be performed in conjunction withan external device, the processor 1080 may generate a control signal forcontrolling the external device and transmit the generated controlsignal to the external device.

The processor 1080 may acquire intention information with respect to auser input and determine the user's requirements based on the acquiredintention information.

The processor 1080 may acquire the intention information correspondingto the user input by using at least one of a speech to text (STT) enginefor converting a speech input into a text string or a natural languageprocessing (NLP) engine for acquiring intention information of a naturallanguage.

At least one of the STT engine or the NLP engine may be configured as anANN, at least part of which is trained according to the machine learningalgorithm. At least one of the STT engine or the NLP engine may betrained by the learning processor, a learning processor of the AIserver, or distributed processing of the learning processors. Forreference, specific components of the AI server are illustrated in FIG.11.

The processor 1080 may collect history information including theoperation contents of the AI device 1000 or the user's feedback on theoperation and may store the collected history information in the memory1070 or the learning processor 1030 or transmit the collected historyinformation to the external device such as the AI server. The collectedhistory information may be used to update the learning model.

The processor 1080 may control at least a part of the components of AIdevice 1000 so as to drive an application program stored in the memory1070. Furthermore, the processor 1080 may operate two or more of thecomponents included in the AI device 1000 in combination so as to drivethe application program.

FIG. 11 illustrates an AI server 1120 according to an embodiment of thepresent disclosure.

Referring to FIG. 11, the AI server 1120 may refer to a device thattrains an ANN by a machine learning algorithm or uses a trained ANN. TheAI server 1120 may include a plurality of servers to perform distributedprocessing, or may be defined as a 5G network. The AI server 1120 may beincluded as part of the AI device 1100, and perform at least part of theAI processing.

The AI server 1120 may include a communication unit 1121, a memory 1123,a learning processor 1122, a processor 1126, and so on.

The communication unit 1121 may transmit and receive data to and from anexternal device such as the AI device 1100.

The memory 1123 may include a model storage 1124. The model storage 1124may store a model (or an ANN 1125) which has been trained or is beingtrained through the learning processor 1122.

The learning processor 1122 may train the ANN 1125 by training data. Thelearning model may be used, while being loaded on the AI server 1120 ofthe ANN, or on an external device such as the AI device 1110.

The learning model may be implemented in hardware, software, or acombination of hardware and software. If all or part of the learningmodel is implemented in software, one or more instructions of thelearning model may be stored in the memory 1123.

The processor 1126 may infer a result value for new input data by usingthe learning model and may generate a response or a control commandbased on the inferred result value.

FIG. 12 illustrates an AI system according to an embodiment of thepresent disclosure.

Referring to FIG. 12, in the AI system, at least one of an AI server1260, a robot 1210, a self-driving vehicle 1220, an XR device 1230, asmartphone 1240, or a home appliance 1250 is connected to a cloudnetwork 1200. The robot 1210, the self-driving vehicle 1220, the XRdevice 1230, the smartphone 1240, or the home appliance 1250, to whichAI is applied, may be referred to as an AI device.

The cloud network 1200 may refer to a network that forms part of cloudcomputing infrastructure or exists in the cloud computinginfrastructure. The cloud network 1200 may be configured by using a 3Gnetwork, a 4G or LTE network, or a 5G network.

That is, the devices 1210 to 1260 included in the AI system may beinterconnected via the cloud network 1200. In particular, each of thedevices 1210 to 1260 may communicate with each other directly or througha BS.

The AI server 1260 may include a server that performs AI processing anda server that performs computation on big data.

The AI server 1260 may be connected to at least one of the AI devicesincluded in the AI system, that is, at least one of the robot 1210, theself-driving vehicle 1220, the XR device 1230, the smartphone 1240, orthe home appliance 1250 via the cloud network 1200, and may assist atleast part of AI processing of the connected AI devices 1210 to 1250.

The AI server 1260 may train the ANN according to the machine learningalgorithm on behalf of the AI devices 1210 to 1250, and may directlystore the learning model or transmit the learning model to the AIdevices 1210 to 1250.

The AI server 1260 may receive input data from the AI devices 1210 to1250, infer a result value for received input data by using the learningmodel, generate a response or a control command based on the inferredresult value, and transmit the response or the control command to the AIdevices 1210 to 1250.

Alternatively, the AI devices 1210 to 1250 may infer the result valuefor the input data by directly using the learning model, and generatethe response or the control command based on the inference result.

Hereinafter, various embodiments of the AI devices 1210 to 1250 to whichthe above-described technology is applied will be described. The AIdevices 1210 to 1250 illustrated in FIG. 12 may be regarded as aspecific embodiment of the AI device 1000 illustrated in FIG. 10.

<AI+XR>

The XR device 1230, to which AI is applied, may be configured as a HMD,a HUD provided in a vehicle, a TV, a portable phone, a smartphone, acomputer, a wearable device, a home appliance, a digital signage, avehicle, a fixed robot, a mobile robot, or the like.

The XR device 1230 may acquire information about a surrounding space ora real object by analyzing 3D point cloud data or image data acquiredfrom various sensors or an external device and thus generating positiondata and attribute data for the 3D points, and may render an XR objectto be output. For example, the XR device 1230 may output an XR objectincluding additional information about a recognized object incorrespondence with the recognized object.

The XR device 1230 may perform the above-described operations by usingthe learning model composed of at least one ANN. For example, the XRdevice 1230 may recognize a real object from 3D point cloud data orimage data by using the learning model, and may provide informationcorresponding to the recognized real object. The learning model may betrained directly by the XR device 1230 or by the external device such asthe AI server 1260.

While the XR device 1230 may operate by generating a result by directlyusing the learning model, the XR device 1230 may operate by transmittingsensor information to the external device such as the AI server 1260 andreceiving the result.

<AI+Robot+XR>

The robot 1210, to which AI and XR are applied, may be implemented as aguide robot, a delivery robot, a cleaning robot, a wearable robot, anentertainment robot, a pet robot, an unmanned flying robot, a drone, orthe like.

The robot 1210, to which XR is applied, may refer to a robot to becontrolled/interact within an XR image. In this case, the robot 1210 maybe distinguished from the XR device 1230 and interwork with the XRdevice 1230.

When the robot 1210 to be controlled/interact within an XR imageacquires sensor information from sensors each including a camera, therobot 1210 or the XR device 1230 may generate an XR image based on thesensor information, and the XR device 1230 may output the generated XRimage. The robot 1210 may operate based on the control signal receivedthrough the XR device 1230 or based on the user's interaction.

For example, the user may check an XR image corresponding to a view ofthe robot 1210 interworking remotely through an external device such asthe XR device 1210, adjust a self-driving route of the robot 1210through interaction, control the operation or driving of the robot 1210,or check information about an ambient object around the robot 1210.

AI+Self-Driving+XR>

The self-driving vehicle 1220, to which AI and XR are applied, may beimplemented as a mobile robot, a vehicle, an unmanned flying vehicle, orthe like.

The self-driving driving vehicle 1220, to which XR is applied, may referto a self-driving vehicle provided with a means for providing an XRimage or a self-driving vehicle to be controlled/interact within an XRimage. Particularly, the self-driving vehicle 1220 to becontrolled/interact within an XR image may be distinguished from the XRdevice 1230 and interwork with the XR device 1230.

The self-driving vehicle 1220 provided with the means for providing anXR image may acquire sensor information from the sensors each includinga camera and output the generated XR image based on the acquired sensorinformation. For example, the self-driving vehicle 1220 may include anHUD to output an XR image, thereby providing a passenger with an XRobject corresponding to a real object or an object on the screen.

When the XR object is output to the HUD, at least part of the XR objectmay be output to be overlaid on an actual object to which thepassenger's gaze is directed. When the XR object is output to a displayprovided in the self-driving vehicle 1220, at least part of the XRobject may be output to be overlaid on the object within the screen. Forexample, the self-driving vehicle 1220 may output XR objectscorresponding to objects such as a lane, another vehicle, a trafficlight, a traffic sign, a two-wheeled vehicle, a pedestrian, a building,and so on.

When the self-driving vehicle 1220 to be controlled/interact within anXR image acquires sensor information from the sensors each including acamera, the self-driving vehicle 1220 or the XR device 1230 may generatethe XR image based on the sensor information, and the XR device 1230 mayoutput the generated XR image. The self-driving vehicle 1220 may operatebased on a control signal received through an external device such asthe XR device 1230 or based on the user's interaction.

VR, AR, and MR technologies of the present disclosure are applicable tovarious devices, particularly, for example, a HMD, a HUD attached to avehicle, a portable phone, a tablet PC, a laptop computer, a desktopcomputer, a TV, and a signage. The VR, AR, and MR technologies may alsobe applicable to a device equipped with a flexible or rollable display.

The above-described VR, AR, and MR technologies may be implemented basedon CG and distinguished by the ratios of a CG image in an image viewedby the user.

That is, VR provides a real object or background only in a CG image,whereas AR overlays a virtual CG image on an image of a real object.

MR is similar to AR in that virtual objects are mixed and combined witha real world. However, a real object and a virtual object created as aCG image are distinctive from each other and the virtual object is usedto complement the real object in AR, whereas a virtual object and a realobject are handled equally in MR. More specifically, for example, ahologram service is an MR representation.

These days, VR, AR, and MR are collectively called XR withoutdistinction among them. Therefore, embodiments of the present disclosureare applicable to all of VR, AR, MR, and XR.

For example, wired/wireless communication, input interfacing, outputinterfacing, and computing devices are available as hardware(HW)-related element techniques applied to VR, AR, MR, and XR. Further,tracking and matching, speech recognition, interaction and userinterfacing, location-based service, search, and AI are available assoftware (SW)-related element techniques.

Particularly, the embodiments of the present disclosure are intended toaddress at least one of the issues of communication with another device,efficient memory use, data throughput decrease caused by inconvenientuser experience/user interface (UX/UI), video, sound, motion sickness,or other issues.

FIG. 13 is a block diagram illustrating an XR device according toembodiments of the present disclosure. The XR device 1300 includes acamera 1310, a display 1320, a sensor 1330, a processor 1340, a memory1350, and a communication module 1360. Obviously, one or more of themodules may be deleted or modified, and one or more modules may be addedto the modules, when needed, without departing from the scope and spiritof the present disclosure.

The communication module 1360 may communicate with an external device ora server, wiredly or wirelessly. The communication module 1360 may use,for example, Wi-Fi, Bluetooth, or the like, for short-range wirelesscommunication, and for example, a 3GPP communication standard forlong-range wireless communication. LTE is a technology beyond 3GPP TS36.xxx Release 8. Specifically, LTE beyond 3GPP TS 36.xxx Release 10 isreferred to as LTE-A, and LTE beyond 3GPP TS 36.xxx Release 13 isreferred to as LTE-A pro. 3GPP 5G refers to a technology beyond TS36.xxx Release 15 And a technology beyond TS 38.XXX Release 15.Specifically, the technology beyond TS 38.xxx Release 15 is referred toas 3GPP NR, and the technology beyond TS 36.xxx Release 15 is referredto as enhanced LTE. “xxx” represents the number of a technicalspecification. LTE/NR may be collectively referred to as a 3GPP system.

The camera 1310 may capture an ambient environment of the XR device 1300and convert the captured image to an electric signal. The image, whichhas been captured and converted to an electric signal by the camera1310, may be stored in the memory 1350 and then displayed on the display1320 through the processor 1340. Further, the image may be displayed onthe display 1320 by the processor 1340, without being stored in thememory 1350. Further, the camera 110 may have a field of view (FoV). TheFoV is, for example, an area in which a real object around the camera1310 may be detected. The camera 1310 may detect only a real objectwithin the FoV. When a real object is located within the FoV of thecamera 1310, the XR device 1300 may display an AR object correspondingto the real object. Further, the camera 1310 may detect an angle betweenthe camera 1310 and the real object.

The sensor 1330 may include at least one sensor. For example, the sensor1330 includes a sensing means such as a gravity sensor, a geomagneticsensor, a motion sensor, a gyro sensor, an accelerator sensor, aninclination sensor, a brightness sensor, an altitude sensor, anolfactory sensor, a temperature sensor, a depth sensor, a pressuresensor, a bending sensor, an audio sensor, a video sensor, a globalpositioning system (GPS) sensor, and a touch sensor. Further, althoughthe display 1320 may be of a fixed type, the display 1320 may beconfigured as a liquid crystal display (LCD), an organic light emittingdiode (OLED) display, an electroluminescent display (ELD), or a microLED (M-LED) display, to have flexibility. Herein, the sensor 1330 isdesigned to detect a bending degree of the display 1320 configured asthe afore-described LCD, OLED display, ELD, or M-LED display.

The memory 1350 is equipped with a function of storing all or a part ofresult values obtained by wired/wireless communication with an externaldevice or a service as well as a function of storing an image capturedby the camera 1310. Particularly, considering the trend toward increasedcommunication data traffic (e.g., in a 5G communication environment),efficient memory management is required. In this regard, a descriptionwill be given below with reference to FIG. 14.

FIG. 14 is a detailed block diagram of the memory 1350 illustrated inFIG. 13. With reference to FIG. 14, a swap-out process between a randomaccess memory (RAM) and a flash memory according to an embodiment of thepresent disclosure will be described.

When swapping out AR/VR page data from a RAM 1410 to a flash memory1420, a controller 1430 may swap out only one of two or more AR/VR pagedata of the same contents among AR/VR page data to be swapped out to theflash memory 1420.

That is, the controller 1430 may calculate an identifier (e.g., a hashfunction) that identifies each of the contents of the AR/VR page data tobe swapped out, and determine that two or more AR/VR page data havingthe same identifier among the calculated identifiers contain the samecontents. Accordingly, the problem that the lifetime of an AR/VR deviceincluding the flash memory 1420 as well as the lifetime of the flashmemory 1420 is reduced because unnecessary AR/VR page data is stored inthe flash memory 1420 may be overcome.

The operations of the controller 1430 may be implemented in software orhardware without departing from the scope of the present disclosure.More specifically, the memory illustrated in FIG. 14 is included in aHMD, a vehicle, a portable phone, a tablet PC, a laptop computer, adesktop computer, a TV, a signage, or the like, and executes a swapfunction.

A device according to embodiments of the present disclosure may process3D point cloud data to provide various services such as VR, AR, MR, XR,and self-driving to a user.

A sensor collecting 3D point cloud data may be any of, for example, aLiDAR, a red, green, blue depth (RGB-D), and a 3D laser scanner. Thesensor may be mounted inside or outside of a HMD, a vehicle, a portablephone, a tablet PC, a laptop computer, a desktop computer, a TV, asignage, or the like.

FIG. 15 illustrates a point cloud data processing system.

Referring to FIG. 15, a point cloud processing system 1500 includes atransmission device which acquires, encodes, and transmits point clouddata, and a reception device which acquires point cloud data byreceiving and decoding video data. As illustrated in FIG. 15, pointcloud data according to embodiments of the present disclosure may beacquired by capturing, synthesizing, or generating the point cloud data(S1510). During the acquisition, data (e.g., a polygon file format orstandard triangle format (PLY) file) of 3D positions (x, y,z)/attributes (color, reflectance, transparency, and so on) of pointsmay be generated. For a video of multiple frames, one or more files maybe acquired. Point cloud data-related metadata (e.g., metadata relatedto capturing) may be generated during the capturing. The transmissiondevice or encoder according to embodiments of the present disclosure mayencode the point cloud data by video-based point cloud compression(V-PCC) or geometry-based point cloud compression (G-PCC), and outputone or more video streams (S1520). V-PCC is a scheme of compressingpoint cloud data based on a 2D video codec such as high efficiency videocoding (HEVC) or versatile video coding (VVC), G-PCC is a scheme ofencoding point cloud data separately into two streams: geometry andattribute. The geometry stream may be generated by reconstructing andencoding position information about points, and the attribute stream maybe generated by reconstructing and encoding attribute information (e.g.,color) related to each point. In V-PCC, despite compatibility with a 2Dvideo, much data is required to recover V-PCC-processed data (e.g.,geometry video, attribute video, occupancy map video, and auxiliaryinformation), compared to G-PCC, thereby causing a long latency inproviding a service. One or more output bit streams may be encapsulatedalong with related metadata in the form of a file (e.g., a file formatsuch as ISOBMFF) and transmitted over a network or through a digitalstorage medium (S1530).

The device or processor according to embodiments of the presentdisclosure may acquire one or more bit streams and related metadata bydecapsulating the received video data, and recover 3D point cloud databy decoding the acquired bit streams in V-PCC or G-PCC (S1540). Arenderer may render the decoded point cloud data and provide contentsuitable for VR/AR/MR/service to the user on a display (S1550).

As illustrated in FIG. 15, the device or processor according toembodiments of the present disclosure may perform a feedback process oftransmitting various pieces of feedback information acquired during therendering/display to the transmission device or to the decoding process(S1560). The feedback information according to embodiments of thepresent disclosure may include head orientation information, viewportinformation indicating an area that the user is viewing, and so on.Because the user interacts with a service (or content) provider throughthe feedback process, the device according to embodiments of the presentdisclosure may provide a higher data processing speed by using theafore-described V-PCC or G-PCC scheme or may enable clear videoconstruction as well as provide various services in consideration ofhigh user convenience.

FIG. 16 is a block diagram of an XR device 1600 including a learningprocessor. Compared to FIG. 13, only a learning processor 1670 is added,and thus a redundant description is avoided because FIG. 13 may bereferred to for the other components.

Referring to FIG. 16, the XR device 1600 may be loaded with a learningmodel. The learning model may be implemented in hardware, software, or acombination of hardware and software. If the whole or part of thelearning model is implemented in software, one or more instructions thatform the learning model may be stored in a memory 1650.

According to embodiments of the present disclosure, a learning processor1670 may be coupled communicably to a processor 1640, and repeatedlytrain a model including ANNs by using training data. An ANN is aninformation processing system in which multiple neurons are linked inlayers, modeling an operation principle of biological neurons and linksbetween neurons. An ANN is a statistical learning algorithm inspired bya neural network (particularly the brain in the central nervous systemof an animal) in machine learning and cognitive science. Machinelearning is one field of AI, in which the ability of learning without anexplicit program is granted to a computer. Machine learning is atechnology of studying and constructing a system for learning,predicting, and improving its capability based on empirical data, and analgorithm for the system. Therefore, according to embodiments of thepresent disclosure, the learning processor 1670 may infer a result valuefrom new input data by determining optimized model parameters of an ANN.Therefore, the learning processor 1670 may analyze a device use patternof a user based on device use history information about the user.Further, the learning processor 1670 may be configured to receive,classify, store, and output information to be used for data mining, dataanalysis, intelligent decision, and a machine learning algorithm andtechnique.

According to embodiments of the present disclosure, the processor 1640may determine or predict at least one executable operation of the devicebased on data analyzed or generated by the learning processor 1670.Further, the processor 1640 may request, search, receive, or use data ofthe learning processor 1670, and control the XR device 1600 to perform apredicted operation or an operation determined to be desirable among theat least one executable operation. According to embodiments of thepresent disclosure, the processor 1640 may execute various functions ofrealizing intelligent emulation (i.e., knowledge-based system, reasoningsystem, and knowledge acquisition system). The various functions may beapplied to an adaptation system, a machine learning system, and varioustypes of systems including an ANN (e.g., a fuzzy logic system). That is,the processor 1640 may predict a user's device use pattern based on dataof a use pattern analyzed by the learning processor 1670, and controlthe XR device 1600 to provide a more suitable XR service to the UE.Herein, the XR service includes at least one of the AR service, the VRservice, or the MR service.

FIG. 17 illustrates a process of providing an XR service by the XRservice 1600 of the present disclosure illustrated in FIG. 16.

According to embodiments of the present disclosure, the processor 1670may store device use history information about a user in the memory 1650(S1710). The device use history information may include informationabout the name, category, and contents of content provided to the user,information about a time at which a device has been used, informationabout a place in which the device has been used, time information, andinformation about use of an application installed in the device.

According to embodiments of the present disclosure, the learningprocessor 1670 may acquire device use pattern information about the userby analyzing the device use history information (S1720). For example,when the XR device 1600 provides specific content A to the user, thelearning processor 1670 may learn information about a pattern of thedevice used by the user using the corresponding terminal by combiningspecific information about content A (e.g., information about the agesof users that generally use content A, information about the contents ofcontent A, and content information similar to content A), andinformation about the time points, places, and number of times in whichthe user using the corresponding terminal has consumed content A.

According to embodiments of the present disclosure, the processor 1640may acquire the user device pattern information generated based on theinformation learned by the learning processor 1670, and generate deviceuse pattern prediction information (S1730). Further, when the user isnot using the device 1600, if the processor 1640 determines that theuser is located in a place where the user has frequently used the device1600, or it is almost time for the user to usually use the device 1600,the processor 1640 may indicate the device 1600 to operate. In thiscase, the device according to embodiments of the present disclosure mayprovide AR content based on the user pattern prediction information(S1740).

When the user is using the device 1600, the processor 1640 may checkinformation about content currently provided to the user, and generatedevice use pattern prediction information about the user in relation tothe content (e.g., when the user requests other related content oradditional data related to the current content). Further, the processor1640 may provide AR content based on the device use pattern predictioninformation by indicating the device 1600 to operate (S1740). The ARcontent according to embodiments of the present disclosure may includean advertisement, navigation information, danger information, and so on.

FIG. 18 illustrates the outer appearances of an XR device and a robot.

Component modules of an XR device 1800 according to an embodiment of thepresent disclosure have been described before with reference to theprevious drawings, and thus a redundant description is not providedherein.

The outer appearance of a robot 1810 illustrated in FIG. 18 is merely anexample, and the robot 1810 may be implemented to have various outerappearances according to the present disclosure. For example, the robot1810 illustrated in FIG. 18 may be a drone, a cleaner, a cook root, awearable robot, or the like. Particularly, each component of the robot1810 may be disposed at a different position such as up, down, left,right, back, or forth according to the shape of the robot 1810.

The robot 1810 may be provided, on the exterior thereof, with varioussensors to identify ambient objects. Further, to provide specificinformation to a user, the robot 1810 may be provided with an interfaceunit 1811 on top or the rear surface 1812 thereof.

To sense movement of the robot 1810 and an ambient object, and controlthe robot 1810, a robot control module 1850 is mounted inside the robot1810. The robot control module 1850 may be implemented as a softwaremodule or a hardware chip with the software module implemented therein.The robot control module 1850 may include a deep learner 1851, a sensinginformation processor 1852, a movement path generator 1853, and acommunication module 1854.

The sensing information processor 1852 collects and processesinformation sensed by various types of sensors (e.g., a LiDAR sensor, anIR sensor, an ultrasonic sensor, a depth sensor, an image sensor, and amicrophone) arranged in the robot 1810.

The deep learner 1851 may receive information processed by the sensinginformation processor 1851 or accumulative information stored duringmovement of the robot 1810, and output a result required for the robot1810 to determine an ambient situation, process information, or generatea moving path.

The moving path generator 1852 may calculate a moving path of the robot1810 by using the data calculated by the deep learner 8151 or the dataprocessed by the sensing information processor 1852.

Because each of the XR device 1800 and the robot 1810 is provided with acommunication module, the XR device 1800 and the robot 1810 may transmitand receive data by short-range wireless communication such as Wi-Fi orBluetooth, or 5G long-range wireless communication. A technique ofcontrolling the robot 1810 by using the XR device 1800 will be describedbelow with reference to FIG. 19.

FIG. 19 is a flowchart illustrating a process of controlling a robot byusing an XR device.

The XR device and the robot are connected communicably to a 5G network(S1901). Obviously, the XR device and the robot may transmit and receivedata by any other short-range or long-range communication technologywithout departing from the scope of the present disclosure.

The robot captures an image/video of the surroundings of the robot bymeans of at least one camera installed on the interior or exterior ofthe robot (S1902) and transmits the captured image/video to the XRdevice (S1903). The XR device displays the captured image/video (S1904)and transmits a command for controlling the robot to the robot (S1905).The command may be input manually by a user of the XR device orautomatically generated by AI without departing from the scope of thedisclosure.

The robot executes a function corresponding to the command received instep S1905 (S1906) and transmits a result value to the XR device(S1907). The result value may be a general indicator indicating whetherdata has been successfully processed or not, a current captured image,or specific data in which the XR device is considered. The specific datais designed to change, for example, according to the state of the XRdevice. If a display of the XR device is in an off state, a command forturning on the display of the XR device is included in the result valuein step S1907. Therefore, when an emergency situation occurs around therobot, even though the display of the remote XR device is turned off, anotification message may be transmitted.

AR/VR content is displayed according to the result value received instep S1907 (S1908).

According to another embodiment of the present disclosure, the XR devicemay display position information about the robot by using a GPS moduleattached to the robot.

The XR device 1300 described with reference to FIG. 13 may be connectedto a vehicle that provides a self-driving service in a manner thatallows wired/wireless communication, or may be mounted on the vehiclethat provides the self-driving service. Accordingly, various servicesincluding AR/VR may be provided even in the vehicle that provides theself-driving service.

FIG. 20 illustrates a vehicle that provides a self-driving service.

According to embodiments of the present disclosure, a vehicle 2010 mayinclude a car, a train, and a motor bike as transportation meanstraveling on a road or a railway. According to embodiments of thepresent disclosure, the vehicle 2010 may include all of an internalcombustion engine vehicle provided with an engine as a power source, ahybrid vehicle provided with an engine and an electric motor as a powersource, and an electric vehicle provided with an electric motor as apower source.

According to embodiments of the present disclosure, the vehicle 2010 mayinclude the following components in order to control operations of thevehicle 2010: a user interface device, an object detection device, acommunication device, a driving maneuver device, a main electroniccontrol unit (ECU), a drive control device, a self-driving device, asensing unit, and a position data generation device.

Each of the user interface device, the object detection device, thecommunication device, the driving maneuver device, the main ECU, thedrive control device, the self-driving device, the sensing unit, and theposition data generation device may generate an electric signal, and beimplemented as an electronic device that exchanges electric signals.

The user interface device may receive a user input and provideinformation generated from the vehicle 2010 to a user in the form of aUI or UX. The user interface device may include an input/output (I/O)device and a user monitoring device. The object detection device maydetect the presence or absence of an object outside of the vehicle 2010,and generate information about the object. The object detection devicemay include at least one of, for example, a camera, a LiDAR, an IRsensor, or an ultrasonic sensor. The camera may generate informationabout an object outside of the vehicle 2010. The camera may include oneor more lenses, one or more image sensors, and one or more processorsfor generating object information. The camera may acquire informationabout the position, distance, or relative speed of an object by variousimage processing algorithms. Further, the camera may be mounted at aposition where the camera may secure an FoV in the vehicle 2010, tocapture an image of the surroundings of the vehicle 1020, and may beused to provide an AR/VR-based service. The LiDAR may generateinformation about an object outside of the vehicle 2010. The LiDAR mayinclude a light transmitter, a light receiver, and at least oneprocessor which is electrically coupled to the light transmitter and thelight receiver, processes a received signal, and generates data about anobject based on the processed signal.

The communication device may exchange signals with a device (e.g.,infrastructure such as a server or a broadcasting station), anothervehicle, or a terminal) outside of the vehicle 2010. The drivingmaneuver device is a device that receives a user input for driving. Inmanual mode, the vehicle 2010 may travel based on a signal provided bythe driving maneuver device. The driving maneuver device may include asteering input device (e.g., a steering wheel), an acceleration inputdevice (e.g., an accelerator pedal), and a brake input device (e.g., abrake pedal).

The sensing unit may sense a state of the vehicle 2010 and generatestate information. The position data generation device may generateposition data of the vehicle 2010. The position data generation devicemay include at least one of a GPS or a differential global positioningsystem (DGPS). The position data generation device may generate positiondata of the vehicle 2010 based on a signal generated from at least oneof the GPS or the DGPS. The main ECU may provide overall control to atleast one electronic device provided in the vehicle 2010, and the drivecontrol device may electrically control a vehicle drive device in thevehicle 2010.

The self-driving device may generate a path for the self-driving servicebased on data acquired from the object detection device, the sensingunit, the position data generation device, and so on. The self-drivingdevice may generate a driving plan for driving along the generated path,and generate a signal for controlling movement of the vehicle accordingto the driving plan. The signal generated from the self-driving deviceis transmitted to the drive control device, and thus the drive controldevice may control the vehicle drive device in the vehicle 2010.

As illustrated in FIG. 20, the vehicle 2010 that provides theself-driving service is connected to an XR device 2000 in a manner thatallows wired/wireless communication. The XR device 2000 may include aprocessor 2001 and a memory 2002. While not shown, the XR device 2000 ofFIG. 20 may further include the components of the XR device 1300described before with reference to FIG. 13.

If the XR device 2000 is connected to the vehicle 2010 in a manner thatallows wired/wireless communication. The XR device 2000 mayreceive/process AR/VR service-related content data that may be providedalong with the self-driving service, and transmit the received/processedAR/VR service-related content data to the vehicle 2010. Further, whenthe XR device 2000 is mounted on the vehicle 2010, the XR device 2000may receive/process AR/VR service-related content data according to auser input signal received through the user interface device and providethe received/processed AR/VR service-related content data to the user.In this case, the processor 2001 may receive/process the AR/VRservice-related content data based on data acquired from the objectdetection device, the sensing unit, the position data generation device,the self-driving device, and so on. According to embodiments of thepresent disclosure, the AR/VR service-related content data may includeentertainment content, weather information, and so on which are notrelated to the self-driving service as well as information related tothe self-driving service such as driving information, path informationfor the self-driving service, driving maneuver information, vehiclestate information, and object information.

FIG. 21 illustrates a process of providing an AR/VR service during aself-driving service.

According to embodiments of the present disclosure, a vehicle or a userinterface device may receive a user input signal (S2110). According toembodiments of the present disclosure, the user input signal may includea signal indicating a self-driving service. According to embodiments ofthe present disclosure, the self-driving service may include a fullself-driving service and a general self-driving service. The fullself-driving service refers to perfect self-driving of a vehicle to adestination without a user's manual driving, whereas the generalself-driving service refers to driving a vehicle to a destinationthrough a user's manual driving and self-driving in combination.

It may be determined whether the user input signal according toembodiments of the present disclosure corresponds to the fullself-driving service (S2120). When it is determined that the user inputsignal corresponds to the full self-driving service, the vehicleaccording to embodiments of the present disclosure may provide the fullself-driving service (S2130). Because the full self-driving service doesnot need the user's manipulation, the vehicle according to embodimentsof the present disclosure may provide VR service-related content to theuser through a window of the vehicle, a side mirror of the vehicle, anHMD, or a smartphone (S2130). The VR service-related content accordingto embodiments of the present disclosure may be content related to fullself-driving (e.g., navigation information, driving information, andexternal object information), and may also be content which is notrelated to full self-driving according to user selection (e.g., weatherinformation, a distance image, a nature image, and a voice call image).

If it is determined that the user input signal does not correspond tothe full self-driving service, the vehicle according to embodiments ofthe present disclosure may provide the general self-driving service(S2140). Because the FoV of the user should be secured for the user'smanual driving in the general self-driving service, the vehicleaccording to embodiments of the present disclosure may provide ARservice-related content to the user through a window of the vehicle, aside mirror of the vehicle, an HMD, or a smartphone (S2140).

The AR service-related content according to embodiments of the presentdisclosure may be content related to full self-driving (e.g., navigationinformation, driving information, and external object information), andmay also be content which is not related to self-driving according touser selection (e.g., weather information, a distance image, a natureimage, and a voice call image).

While the present disclosure is applicable to all the fields of 5Gcommunication, robot, self-driving, and AI as described before, thefollowing description will be given mainly of the present disclosureapplicable to an XR device with reference to following figures.

FIG. 22 is a conceptual diagram illustrating an exemplary method forimplementing the XR device using an HMD type according to an embodimentof the present disclosure. The above-mentioned embodiments may also beimplemented in HMD types shown in FIG. 22.

The HMD-type XR device 100 a shown in FIG. 22 may include acommunication unit 110, a control unit 120, a memory unit 130, aninput/output (I/O) unit 140 a, a sensor unit 140 b, a power-supply unit140 c, etc. Specifically, the communication unit 110 embedded in the XRdevice 10 a may communicate with a mobile terminal 100 b by wire orwirelessly.

FIG. 23 is a conceptual diagram illustrating an exemplary method forimplementing an XR device using AR glasses according to an embodiment ofthe present disclosure. The above-mentioned embodiments may also beimplemented in AR glass types shown in FIG. 23.

Referring to FIG. 23, the AR glasses may include a frame, a control unit200, and an optical display unit 300.

Although the frame may be formed in a shape of glasses worn on the faceof the user 10 as shown in FIG. 23, the scope or spirit of the presentdisclosure is not limited thereto, and it should be noted that the framemay also be formed in a shape of goggles worn in close contact with theface of the user 10.

The frame may include a front frame 110 and first and second sideframes.

The front frame 110 may include at least one opening, and may extend ina first horizontal direction (i.e., an X-axis direction). The first andsecond side frames may extend in the second horizontal direction (i.e.,a Y-axis direction) perpendicular to the front frame 110, and may extendin parallel to each other.

The control unit 200 may generate an image to be viewed by the user 10or may generate the resultant image formed by successive images. Thecontrol unit 200 may include an image source configured to create andgenerate images, a plurality of lenses configured to diffuse andconverge light generated from the image source, and the like. The imagesgenerated by the control unit 200 may be transferred to the opticaldisplay unit 300 through a guide lens P200 disposed between the controlunit 200 and the optical display unit 300.

The controller 200 may be fixed to any one of the first and second sideframes. For example, the control unit 200 may be fixed to the inside oroutside of any one of the side frames, or may be embedded in andintegrated with any one of the side frames.

The optical display unit 300 may be formed of a translucent material, sothat the optical display unit 300 can display images created by thecontrol unit 200 for recognition of the user 10 and can allow the userto view the external environment through the opening.

The optical display unit 300 may be inserted into and fixed to theopening contained in the front frame 110, or may be located at the rearsurface (interposed between the opening and the user 10) of the openingso that the optical display unit 300 may be fixed to the front frame110. For example, the optical display unit 300 may be located at therear surface of the opening, and may be fixed to the front frame 110 asan example.

Referring to the XR device shown in FIG. 23, when images are incidentupon an incident region S1 of the optical display unit 300 by thecontrol unit 200, image light may be transmitted to an emission regionS2 of the optical display unit 300 through the optical display unit 300,images created by the controller 200 can be displayed for recognition ofthe user 10.

Accordingly, the user 10 may view the external environment through theopening of the frame 100, and at the same time may view the imagescreated by the control unit 200.

As described above, although methods described herein can be applied toall the 5G communication technology, robot technology, autonomousdriving technology, and Artificial Intelligence (AI) technology,following figures illustrate various examples of the present disclosureapplicable to multimedia devices such as XR devices, digital signage,and TVs for convenience of description. However, it should be understoodthat other embodiments implemented by those skilled in the art bycombining the examples of the following figures with each other byreferring to the examples of the previous figures are also within thescope of the present disclosure.

In some embodiments, the multimedia device (or a device) described inthe following figures can be implemented as any of devices each having adisplay function without departing from the scope or spirit of thepresent disclosure, so that the multimedia device is not limited to theXR device and corresponds to the user equipment (UE) described withrespect to FIGS. 1 to 9 and the multimedia device shown in the followingfigures can additionally perform 5G communication.

FIG. 24 represents 3D image of a user in accordance with someembodiments.

FIG. 24 includes user's 3D images which are seen from different angles.FIG. 24 represents 3D image 2400 includes the glasses (e.g., an image2410, an image 2420, an image 2430) including which have not beenrendered as a 3D image. Thus, such unrealistic 3D image 2400 may causepoor user experience.

Therefore, the XR device for providing XR content determines whetherother objects (e.g., glasses, sunglasses, etc.) except the face areincluded in a 2D image of a user, generates a 3D image based on the 2Dimage that other objects have been removed, generates 3D image relatedto the removed objects by analyzing image of the removed objects, andthen generates a final 3D image by combining two 3D images. In someembodiments, the final 3D image includes not only 3 dimensional image ofthe user's face but also 3 dimensional image of the glasses. In someembodiments, the XR device for providing XR content performs operationsof the AI device described in respect to FIGS. 10-12. Thus, the XRdevice for providing XR content analyzes objects based on a maskimage/video representing difference between the user's 2D image and 2Dimage which the object were removed by using one or more deep learningalgorithms and acquires (obtains) a 3D image in accordance with theanalyzed result. Therefore, the XR device for providing XR content cangenerate 3D image after learning with refined image/video focused on theobjects without using a traditional deep learning method which islearning abstract feature based on raw data so that it can provide theuser's 3D image with higher accuracy. In addition, the XR device forproviding XR content can provide a higher user experience and a varietyof content on the basis of 3D image generated.

FIG. 25 represents a block diagram of XR device for providing XR contentin accordance with some embodiments.

In some embodiments, the XR device 2500 for providing XR content mayinclude an image/video analyzer 2510, a data base 2520, a 3D image/videoprocessor 2530, a display unit (or a display) 2540 and a controller2550. The XR device 2500 for providing XR content may include one ormore modules (not shown), etc. to perform functions/operations asdescribed with respect to FIGS. 1-23. The image/video analyzer 2510 canacquire a user's 2D image and analyze the user's 2D image. In someembodiments, user's 2D image/video as a real 2D image representingactual appearance of the user, is also referred to as a first 2Dimage/video. Image/video analyzer 2510 may include camera sensors andothers to acquire the first 2D image/video. Image/video analyzer 2510can determine whether other objects (e.g., a second object that the useris wearing, not the user's face, such as glasses, sunglasses andaccessories etc.) except the face (e.g., a first object) are included inthe first 2D image/video by analyzing the first 2D image/video. Inresponse to determining that there is the second object which is not theface in the first 2D image/video, the image/video analyzer 2510 cantransform the first 2D image/video by using one or more algorithms(e.g., an image-to-image algorithm, CycleGAN algorithm etc.) andgenerate 2D image/video by removing the object (e.g., 2D image/video ofuser's face not wearing glasses). in some embodiments, the 2Dimage/video that the objects were removed from the first 2D image/videois a fake 2D image which is not an actual appearance of the user, andcan be referred to as a second 2D image/video. In some embodiments, theimage/video analyzer 2510 can generate a mask image/video whichrepresents a different image/video between the first 2D image/video andthe second 2D image/video. The image/video analyzer 2510 analyzes anddetermines a type of the object (e.g., a type of frame for glasses, atype of frame for sunglasses, etc.) that the mask image/video representsby using one or more algorithms (e.g., an image classificationalgorithm) and then can transmit the signals asking one or more 3Dimages that represent the relevant objects based on analyzed data toexternal server or data base 2520. In some embodiments, the image/videoanalyzer 2510 may include a removal module to generate the second 2Dimage/video, and a glass type classification module to analyze/determinethe type of the object (e.g., the type of frames for glasses, the typeof frames for sunglasses, etc.) represented by the mask image/video. Itshould be noted that the number of modules, and types of modules aremerely example.

In some embodiments, the data base 2520 can receive and store the first2D image/video or the second 2D image/video from the image/videoanalyzer 2510. In addition, the data base 2520 can store 3D imagepresets that denote the object (e.g., glasses, sunglasses etc. that usermight be wearing). In some embodiments, a 3D image preset may includeone or more 3D images categorized in accordance with the type of theobject (e.g., a shape of the frame for glasses). The data base 2520receives a signal requesting for the 3D image preset from image/videoanalyzer 2510 and transmits the 3D image preset in response to thesignal to 3D image/video processor 2530.

In some embodiments, the 3D image/video processor 2530 receives thesecond 2D image/video from the image/video analyzer 2510 and generates3D image/video which in accordance with this. 3D image/video inaccordance with the second 2D image/video may include a 3 dimensionalavatar, etc. representing a user's face not wearing glasses and bereferred to as a first 3D image/video. The 3D image/video processor 2530can search whether one or more 3D images similar to the object existwithin the 3D preset received from the data base 2520. The 3Dimage/video processor 2530 calculate a similarity between one or more 3Dimages and the object. Furthermore, the 3D image/video processor 2530may set the similarity (e.g., 60%) in order to search 3D image preset,and explore one or more 3D images having similarity of which value isgreater than or equal to a predetermined similarity value. The 3Dimage/video processor 2530 combines the first image/video with thesearched 3D image/video and generates the final 3D image/video. In someembodiments, the final 3D image/video can represent a 3 dimensionalimage of the user's face and a 3 dimensional image of the object (e.g.,glasses, sunglasses, etc.) and may be referred to as a second 3Dimage/video. In some embodiments, the 3D image/video processor 2530 cangenerate the final 3D image by combining the first 3D image with a 3Dimage that is selected by the user in accordance with a user inputsignal for selecting one of the plural 3D images within the 3D preset.The predetermined similarity value may be set/changed in accordance witha user input signal.

In some embodiments, the display unit 2540 provides the third 3Dimage/video and/or XR content including the third 3D image/video. Inaddition, the display unit 2540 may provide XR content to display whollywithout combining the first 3D image/video with 3D image preset in orderto receive the user input signal for selecting one of the plural 3Dimages within the 3D preset. In some embodiments, the display unit 2540may display XR content including one or more 3D images generated inaccordance with a user input signal (e.g., a user input signal forgenerating a final 3D image by using the selected 3D image, etc.), TheXR content may include icons representing the 3D image and informationrelated to 3D image (e.g., a similarity value with relevant objectetc.). The display unit 2540 can display a user touch interface in orderto receive a user touch input.

In some embodiments, the controller 2550 can control multipleoperations/functions of the XR device for providing XR content. Thecontroller 2550 can communicate with the image/video analyzer 2510, thedata base 2520, the 3D image/video processor 2530 and the display unit2540. In some embodiments, the controller 2550 controls the image/videoanalyzer 2510 to acquire and analyze the first 2D image/video, and thedata base 2520 to acquire 3D image preset in accordance with requestfrom image/video analyzer 2510. Furthermore, the controller 2550 cancontrol the display unit 2540 to provide XR content that the first 3Dimage/video and 3D image preset corresponding to the second 2Dimage/video respectively, the second 3D image/video and/or XR contentincluding the second 3D image/video.

In some embodiments, one or more elements of the XR device 2500 (e.g.,the image/video analyzer 2510, the data base 2520, the 3D image/videoprocessor 2530, the display unit 2540 and the controller 2550) can beimplemented in hardware, software or firmware or a combination thereof,including one or more processors and/or integrated circuits that arecommunicable with a memory (not shown) of the XR device for providing XRcontent 2500. The one or more processors run or execute various softwareprograms and/or sets of instructions stored in the memory to performvarious functions for the XR device 2500 for providing XR content and toprocess data. In some embodiments, the memory optionally includeshigh-speed random access memory and optionally also includesnon-volatile memory, such as one or more magnetic disk storage devices,flash memory devices, or other non-volatile solid-state memory devices.In some embodiments, the memory may store one or more programs includinginstructions needed to perform or control operations of the one or moreelements of the XR device 2500 (e.g., the image/video analyzer 2510, thedata base 2520, the 3D image/video processor 2530, the display unit 2540and the controller 2550). In some embodiments, the one or more programsmay include instructions needed to execute one or more algorithms. Itshould be noted that the XR device 2500 can perform the details of othermethods/functions described above (e.g., methods described in FIGS. 1 to23) in an analogous manner. Although not shown in the drawings, the XRdevice 2400 may further include one or more modules for performing theoperations/functions as described with respect to FIGS. 1 to 23, and thelike.

FIG. 26 represents a method for providing XR content in accordance withsome embodiments.

FIG. 26 represents a process that an XR device for providing XR content(e.g., the XR device for providing XR content 2500) generates a 3Dimage/video of a user wearing glasses (e.g., the second 3D image/video,avatar etc. described in respect to FIG. 25). The XR device forproviding XR content (e.g., the image/video analyzer 2510) acquires a 2Dimage of the user wearing glasses (the first 2D image/video described inrespect to FIG. 25) through the camera sensors and others 2600. The XRdevice for providing XR content (e.g., a glass removal module includedin the image/video analyzer 2510 or the image/video analyzer 2510) cantransform a real 2D image/video of the user wearing glasses by using oneor more algorithms 2610 in order to generate a fake 2D image/video(e.g., the second 2D image/video described in respect to FIG. 25)representing an image of a user's face with glasses removed 2610. The XRdevice for providing XR content (e.g., the 3D image/video processor2540) can generate a 3D image/video (e.g., the first 3D image/video)corresponding to the 2D image/video representing the user's image withglasses removed 2620.

In some embodiments, the XR device for providing XR content (e.g., theimage/video analyzer 2510) can generate a mask image/video correspondingto a difference image/video between 2D image/video of the user wearingglasses and the image of user with glasses removed 2630. The XR devicefor providing XR content outputs a type of glasses that mask image/videorepresents by using one or more algorithms (e.g., an imageidentification algorithm) 2640. Type of glasses may be set based on aframe category, or a brand of glasses, etc. The XR device for providingXR content (e.g., image/video processor 2530) can acquire a 3D presetincluding one or more 3D images corresponding to the type of categorizedglasses, then search whether a 3D image/video similar to the type ofglasses categorized exists 2650. The XR device for providing XR contentcan calculate a similarity between one or more 3D images/videos in 3Dpreset and the type of categorized glasses. The XR device for providingXR content can search one or more 3D images/videos having similarityhaving a value of similarity which is greater than or equal to apredetermined similarity value.

In some embodiments, the XR device for providing XR content (e.g., theimage/video processor 2530) generates a final 3D image/video (e.g., thesecond 3D image/video described with respect to FIG. 25) by combiningthe searched 3D image/video and a 3D image/video which corresponds to a2D image/video representing an image of user glasses removed. FIG. 26represents the final 3D image/video based on 3D image/video which hasthe biggest value of similarity. Thus, the final 3D image/video provideshigher user experience as it represents a 3 dimensional image/video ofglasses as well as user's face.

FIG. 27 represents a training process of deep learning operations of afirst network and a second network in accordance with some embodiments.

As described above with respect to FIGS. 25-26, the XR device forproviding XR content (or the image/video analyzer 2510) generates a fakeimage representing an image of a user not wearing glasses from a realimage of user wearing glasses by using one or more algorithms. In orderto the fake image with high accuracy, generated fake image should besimilar to a real image of a user not wearing glasses. Thus, the XRdevice for providing XR content restores the real image by usinggenerated fake image, and learns whether the restored image and the realimage are the same, and then may perform operations for minimizingCycle-consistency loss between the restored image and the real image.The XR device for providing XR content can train a generator 2700-1,2700-2 and a discriminator 2710-1 included in a first network 2700 and asecond network 2710. The first network 2700 and the second network 2710can be trained by a cycle method simultaneously.

In some embodiments, the first network 2700 is used for learning processof the generator 2700-1, 2700-2. The real images of user wearing glasses2701 are real images of domain X, a generator Gxy 2700 can transform thereal images of domain X 2701 into fake images of domain Y 2702. The fakeimages of domain Y 2702 correspond to images of users not wearingglasses. The generator Gyx 2700-1 can transform the fake images ofdomain Y 2702 into domain X again, and then generate the restored imagesof domain X 2703. In such configuration, the generator 2700, 2700-1 canlearn a process to minimize the cycle-consistency loss between the realimages 2701 and the restored images 2703.

In some embodiments, the second network 2710 is used for a learningprocess of the discriminator 2710-1. In some embodiments, thediscriminator 2710-1 obtains the fake images of domain Y 2702, comparesthe fake images of domain Y 2702 with the real images of domain Y 2711and then performs the learning process that distinguishes whetherinputted the fake images 2702 are real image or fake image. Thegenerator 2700-1, 2700-2 can learn the process of generating a fakeimage which is similar to a real image to prevent the discriminator 2710from distinguishing the real image from the fake image.

FIG. 28 represents XR content in accordance with some embodiments.

In some embodiments, the XR device for providing XR content provides XRcontent 2800 in order to display 3D image/video representing an image ofa user not wearing glasses (e.g., the first 3D image/video illustratedin FIG. 25) and 3D image preset including one or more 3D images/videoscorresponding to an image of user's glasses. XR content 2800 may include3D image/video corresponding to the image of user not wearing glasses2810, one or more 3D images/videos corresponding to the image of user'sglasses 2820 and information on respective 3D image/video 2830. Theinformation on respective 3D image/video 2830 may include information onsimilarity of the 3D image/video. In response to a user input signal forselecting a 3D image/video of 3D images/videos 2820, the XR device forproviding XR content generates and provides the final 3D image/video bycombining 3D image/video selected by the user signal and 3D image/videocorresponding to the image of user not wearing glasses 2810. Inaddition, the XR device for providing XR content analyzes a user's faceshape etc. based on a 2D image/video of user not wearing glasses (e.g.,the second 2D image described with respect to FIG. 25, the fake image2702, or the 3D image/video 2810) and then provides one or more 3Dimages/videos 2820 regarding glasses that suit the user's face shape.The XR device for providing XR content may acquire matching informationon 3D images/videos of one or more glasses and the face shape of user.The XR device for providing XR content may recommend/provide 3Dimages/videos of at least one glasses having matching information thatcorrespond to a value which is greater than or equal to a predeterminedmatching base value. In some embodiments, the information on respective3D image/video 2830 may include information on glasses that therespective 3D image represents (e.g., information on brand, a price, ashopping mall link that is possible to buy the glasses, etc.). inresponse to a user input signal for selecting one 3D image/video of 3Dimages/videos 2820, the XR device for providing XR content generates andprovides the final 3D image/video representing image of user wearingglasses by combining 3D image/video chosen by the user input signal and3D image/video corresponding to the image of user not wearing glasses2810.

FIG. 29 is a flow diagram of a method for providing XR content inaccordance with some embodiments.

The flow diagram 2900 represents a method for XR content by the XRdevice as described with respect to FIGS. 24-28 in accordance with someembodiments.

The XR device (or the image/video analyzer 2510) for providing XRcontent acquires (obtains) a first 2D image representing image of user'sface 2910. The first 2D image may be a real 2D image representing actualappearance of the user before XR device for providing XR content (e.g.,real images explained in FIG. 27 2710). The details will be omittedbecause they are the same as explained in FIG. 25 or FIG. 28.

XR device for providing XR content determines whether there is theobject which is not the user's face within acquired the first 2D image(2920). The object may include the object, not the user's face, such asglasses, sunglasses and accessories etc. that user is wearing.

XR device (or image/video analyzer 2510) for providing XR content, as aresult of determination in the case of the object included, can generatethe second 2D image representing user's face from which the object wasremoved based on the first 2D image (2930). XR device for providing XRcontent transforms the first 2D image and generates 2D image with theobject removed by using one or more algorithms (e.g., image-to-imagetranslation algorithm, CycleGAN algorithm etc.). The second 2D imagemeans the fake image, not the user's actual appearance. The details forcreating the second 2D image will be omitted because they are the sameas explained in FIG. 25 or FIG. 27.

XR device (or the 3D image/video processor 2530) for providing XRcontent can generate the first 3D image in accordance with generated 2Dimage (2940). XR device for providing XR content can display thegenerated the first 3D image only. The details will be omitted becausethey are the same as explained in FIG. 25 or FIG. 28.

XR device (or the 3D image/video processor 2530) for providing XRcontent generates mask image representing difference between the first2D image and the second 2D image (2950).

XR device (or image/video analyzer 2510) for providing XR contentdetermines the type of object based on mask image generated (2960). XRdevice for providing XR content analyzes/determines the type of theobject (e.g., type of frame for glasses, sunglasses etc.) that maskimage/video represents by using one or more algorithms (e.g., ImageClassification algorithm). The details will be omitted because they arethe same as explained in FIG. 25 or FIG. 28.

XR device (or image/video analyzer 2510) for providing XR contentacquires 3D image preset including one or more 3D images in accordancewith the type of determined object (2970). XR device for providing XRcontent can search whether one or more 3D images that are determined tobe similar to the relevant object within 3D image preset received fromdata base 2520 exist. XR device for providing XR content calculates thesimilarity between one or more 3D images and relevant object. Inaddition, XR device for providing XR content predetermines or changesthe similarity value (e.g., 60%) for 3D image preset search and searchesone or more 3D images having the similarity which is greater than orequal to the predetermined similarity value. XR device for providing XRcontent analyzes user's face shape based on 2D image/video of user notwearing glasses (e.g., the second 2D image in FIG. 25, fake image 2702in FIG. 27), or 3D image/video 2810 and then acquires 3D presetincluding 3D images/videos regarding one or more glasses that suituser's face shape. XR device for providing XR content may acquirematching information on 3D images/videos of one or more glasses and faceshape of user, and recommend/provide 3D images/videos of at least oneglasses having matching information in accordance with value which isgreater than or equal to predetermined matching base value. The detailswill be omitted because they are the same as explained in FIG. 25 orFIG. 28.

FIG. 30 is a conceptual diagram illustrating an exemplary case in whichthe XR device is applied to a clothing-related device according to anembodiment of the present disclosure.

Referring to FIG. 30, the embodiments of the present disclosure can beapplied not only to the XR device, but also to various clothing-relateddevices.

The clothing-related device may refer to, for example, a product fordry-cleaning, drying, sterilizing, deodorizing, smoothing (pressing out)clothing, and the like, which is usually installed at home. Of course,the clothing-related devices may also be installed elsewhere. However,the above-mentioned clothing-related device may be called a styler bysome companies, or may also be called an air dresser by other companies.

Additional explanations for better understanding of the presentdisclosure will be given with reference to FIG. 30. If a user 100 movescloser to the clothing-related device (e.g., a styler, an air dresser,or the like), the clothing-related device may recognize the presence ofthe user 100 using a camera or sensor embedded therein.

A display 200 installed at a front surface of the clothing-relateddevice may display an avatar related to the recognized user 100, and mayfurther display a graphic image representing that the user 100 virtuallywears a desired clothing, a hat, etc. As can be seen from FIG. 30,although the real user 100 does not actually wear the hat, it can beconfirmed that the avatar appearing on the display 200 is wearing avirtual hat. Further, when the user 100 is not recognized, the display200 may also act as a mirror only.

Finally, although FIG. 30 assumes that the display 200 was exposed tothe front surface of the clothing-related device for convenience ofdescription, the scope or spirit of the present disclosure is notlimited thereto, and the display 200 may also be embedded in theclothing-related device in a manner that the user who opens the door ofthe clothing-related device can view the embedded display.

The various elements of the XR device shown in FIGS. 1 to 30 areimplemented in hardware, software, firmware or a combination thereof.The various elements of the XR device are implemented on a single chipsuch as a hardware circuit. In some embodiments, they are, optionally,implemented on separate chips. In some embodiments, at least one of theelements of the XR device may be constructed in one or more processorscapable of executing one or more programs including instructions ofperforming or causing performance of the operations of any of themethods described herein.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first imagecould be termed a second image, and, similarly, a second image could betermed a first image, without departing from the scope of the variousdescribed embodiments. The first image and the second image are bothimages, but they are not the same image, unless the context clearlyindicates otherwise.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context. Similarly,the phrase “when it is determined” or “when [a stated condition orevent] is detected” is, optionally, construed to mean “upon determining”or “in response to determining” or “upon detecting [the stated conditionor event]” or “in response to detecting [the stated condition orevent],” depending on the context.

What is claimed is:
 1. A method for providing extended reality (XR)content, the method comprising: obtaining a first two-dimensional (2D)image representing a user's face; determining whether an object which isnot the user's face is included in the first 2D image; in response todetermining that the object is included in the first 2D image,generating a second 2D image representing the user's face without theobject based on the first 2D image; generating a first three-dimensional(3D) image corresponding to the generated second 2D image; generating amask image representing a difference between the first 2D image and thesecond 2D image; determining a type of object based on the generatedmask image; obtaining a 3D preset including one or more 3D imagescorresponding to the determined type of object; generating a second 3Dimage by combining at least one of the one or more 3D images with thefirst 3D image; providing XR content including the generated second 3Dimage; transforming the second 2D image into the first 2D image againand then generating a restored image of the first 2D image through afirst network; and processing to minimize a cycle-consistency lossbetween the first 2D image and the restored image.
 2. The method ofclaim 1, wherein the object includes at least one of glasses worn by theuser, sunglasses worn by the user, and accessories worn by the user. 3.The method of claim 2, wherein the obtaining the 3D preset including theone or more 3D images corresponding to the determined type of objectincludes: calculating similarity between the object and one of the oneor more 3D images respectively.
 4. The method of claim 3, wherein theobtaining the 3D preset including the one or more 3D imagescorresponding to the determined type of object includes: comparing eachone of the calculated similarities with a predetermined similarityvalue; and selecting a 3D image of which calculated similarity has avalue that is greater than or equal to the predetermined similarityvalue.
 5. The method of claim 4, wherein the XR content further includesinformation related to the calculated similarity.
 6. The method of claim2, wherein the obtaining the 3D preset including the one or more 3Dimages corresponding to the determined type of object includes:analyzing a shape of the user's face based on the first 3D image or thesecond 2D image and obtaining matching information between the shape ofthe user's face and one of the one or more 3D images respectively; andselecting a 3D image of which matching information has a value that isgreater than or equal to a predetermined matching base value.
 7. Themethod of claim 6, wherein the XR content further includes the matchinginformation.
 8. The method of claim 1, wherein the generating the second3D image by combining the at least one of the one or more 3D images withthe first 3D image includes: receiving a user input signal for selectinga 3D image in the 3D preset; and combining the first 3D image and theselected 3D image in accordance with the user input signal.
 9. An XRdevice for providing extended reality (XR) content, the XR devicecomprising: an image/video analyzer configured to: obtain a firsttwo-dimensional (2D) image representing a user's face, determine whetheran object which is not the user's face is included in the first 2Dimage, wherein in response to determining that the object is included inthe first 2D image, the image/video analyzer is further configured to:generate a second 2D image representing the user's face without theobject based on the first 2D image, generate a mask image representing adifference between the first 2D image and the second 2D image anddetermine a type of object based on the generated mask image; athree-dimensional (3D) image/video processor configured to: generate afirst 3D image corresponding to the generated second 2D image, obtain a3D preset including one or more 3D images corresponding to thedetermined type of object, and generate a second 3D image by combiningat least one of the one or more 3D images with the first 3D image; afirst network configured to: transform the second 2D image into thefirst 2D image again and then generate a restored image of the first 2Dimage through a first network; and process to minimize acycle-consistency loss between the first 2D image and the restoredimage; and a display configured to providing XR content including thegenerated second 3D image.
 10. The XR device of claim 9, wherein theobject includes at least one of glasses worn by the user, sunglassesworn by the user, and accessories worn by the user.
 11. The XR device ofclaim 10, wherein the 3D image/video processor is further configured tocalculate similarity between the object and the one or more 3D images.12. The XR device of claim 11, wherein the 3D image/video processor isfurther configured to compare each one of the calculated similaritieswith a predetermined similarity value and select a 3D image of whichcalculated similarity has a value that is greater than or equal to thepredetermined similarity value.
 13. The XR device of claim 12, whereinthe XR content further includes information related to the calculatedsimilarity.
 14. The XR device of claim 10, wherein the 3D image/videoprocessor is further configured to analyze a shape of the user's facebased on the first 3D image or the second 2D image, obtain matchinginformation between the shape of the user's face and one of the one ormore 3D images respectively and select a 3D image of which matchinginformation has a value that is greater than or equal to a predeterminedmatching base value.
 15. The XR device of claim 14, wherein the XRcontent further includes the matching information.
 16. The XR device ofclaim 9, wherein: in response to a received user input signal forselecting a 3D image in the 3D preset, the 3D image/video processor isfurther configured to combine the first 3D image and the selected 3Dimage in accordance with the user input signal.
 17. An extended reality(XR) device, comprising: one or more processors; a display; and a memorystoring one or more programs, wherein the one or more programs areconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: determining whether an object whichis not a user's face is included in a first two-dimensional (2D) imagerepresenting the user's face; in response to determining that the objectis included in the first 2D image, generating a second 2D imagerepresenting the user's face without the object based on the first 2Dimage; generating a first three-dimensional (3D) image corresponding tothe generated second 2D image; generating a mask image representing adifference between the first 2D image and the second 2D image;determining a type of object based on the generated mask image;obtaining a 3D preset including one or more 3D images corresponding tothe determined type of object; generating a second 3D image by combiningat least one of the one or more 3D images with the first 3D image,wherein the display is further configured to provide XR contentincluding the generated second 3D image; transforming the second 2Dimage into the first 2D image again and then generating a restored imageof the first 2D image through a first network; and processing tominimize a cycle-consistency loss between the first 2D image and therestored image.
 18. The XR device of claim 17, wherein the one or moreprograms include instructions for: calculating similarity between theobject and the one or more 3D images, comparing each one of thecalculated similarities with a predetermined similarity value; andselecting a 3D image of which calculated similarity has a value that isgreater than or equal to the predetermined similarity value.
 19. The XRdevice of claim 17, wherein the one or more programs includeinstructions for: analyzing a shape of the user's face based on thefirst 3D image or the second 2D image and obtaining matching informationbetween the shape of the user's face and one of the one or more 3Dimages respectively; and selecting a 3D image of which matchinginformation has a value that is greater than or equal to a predeterminedmatching base value.
 20. The XR device of claim 17, wherein: in responseto a received user input signal for selecting a 3D image in the 3Dpreset, the display is further configured to provide a third 3D imagegenerated by combining the first 3D image and the selected 3D image inaccordance with the user input signal.